Análisis de las pruebas realizadas con Blosc¶
Objetivos del análisis¶
- Relacionar el tamaño de bloque con las medidas de compresión y decompresión.
- Comprobar el comportamiento de los niveles de compresión sobre las pruebas.
- Comparar los datos de compresión de tablas normales y columnares.
- ¿Existe correlación entre blosclz o lz4 con nivel de compresión 1 y el resto de codecs?
- [Punto muerto] ¿Existe correlación entre las caracterÃsticas del chunk y las medidas de compresión y decompresión?
Descripción de la muestra¶
El DataFrame en cuestión está formado por las caracterÃsticas extraÃdas de un array de datos al comprimirlo y descomprimirlo mediante blosc. En cada fichero aparecen distintos conjuntos de datos los cuáles dividimos en fragmentos de 16 MegaBytes y sobre los cuales realizamos las pruebas de compresión y decompresión.
Cada fila se corresponde con los datos de realizar los test de compresión sobre un fragmento (chunk) de datos especÃfico con un tamaño de bloque, codec, filtro y nivel de compresión determinados.
| Variable | Descripción |
|---|---|
| Filename | nombre del fichero del que proviene. |
| DataSet | dentro del fichero el conjunto de datos del que proviene. |
| Table | 0 si los datos vienen de un array, 1 si vienen de tablas y 2 para tablas columnares. |
| DType | indica el tipo de los datos. |
| Chunk_Number | número de fragmento dentro del conjunto de datos. |
| Chunk_Size | tamaño del fragmento. |
| Mean | la media. |
| Median | la mediana. |
| Sd | la desviación tÃpica. |
| Skew | el coeficiente de asimetrÃa. |
| Kurt | el coeficiente de apuntamiento. |
| Min | el mÃnimo absoluto. |
| Max | el máximo absoluto. |
| Q1 | el primer cuartil. |
| Q3 | el tercer cuartil. |
| N_Streaks | número de rachas seguidas por encima o debajo de la mediana. |
| Block_Size | el tamaño de bloque que utilizará Blosc para comprimir. |
| Codec | el codec de blosc utilizado. |
| Filter | el filtro de blosc utilizado. |
| CL | el nivel de compresión utilizado. |
| CRate | el ratio de compresión obtenido. |
| CSpeed | la velocidad de compresión obtenida en GB/s. |
| DSpeed | la velocidad de decompresión obtenida en GB/s. |
In [1]:
%matplotlib inline
%config InlineBackend.figure_format='retina'
%load_ext autoreload
%autoreload 2
%load_ext version_information
%version_information numpy, scipy, matplotlib, pandas
Out[1]:
| Software | Version |
|---|---|
| Python | 3.5.2 64bit [MSC v.1900 64 bit (AMD64)] |
| IPython | 5.1.0 |
| OS | Windows 10 10.0.14393 SP0 |
| numpy | 1.11.1 |
| scipy | 0.18.1 |
| matplotlib | 2.0.0 |
| pandas | 0.19.2 |
| Fri Mar 17 09:25:46 2017 Hora estándar romance | |
In [2]:
import os
import sys
sys.path.append("../src/")
from IPython.display import display
import matplotlib
from matplotlib import pyplot as plt
import pandas as pd
import custom_plots as cst
pd.options.display.float_format = '{:,.3f}'.format
matplotlib.rcParams.update({'font.size': 12})
Descripción general¶
Cargamos el csv entero, comprobamos que no faltan campos y mostramos un breve resumen.
In [3]:
# LOAD WHOLE CSV
DF = pd.read_csv('../data/blosc_test_data_v2.csv.gz', sep='\t')
# SORT COLUMNS
DF = DF[cst.COLS]
# CHECK MISSING DATA
if not DF.isnull().any().any():
print('No missing data')
else:
print("Missing data")
No missing data
In [4]:
# SUMMARY OF THE DATAFRAME
display(DF[cst.COLS[5:]].describe())
| Chunk_Size | Mean | Median | Sd | Skew | Kurt | Min | Max | Q1 | Q3 | Block_Size | CL | CRate | CSpeed | DSpeed | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 1,038,420.000 | 1,038,420.000 | 1,038,420.000 | 1,038,420.000 | 1,038,420.000 | 1,038,420.000 | 1,038,420.000 | 1,038,420.000 | 1,038,420.000 | 1,038,420.000 | 1,038,420.000 | 1,038,420.000 | 1,038,420.000 | 1,038,420.000 | 1,038,420.000 |
| mean | 14.905 | 105,388.308 | 78,129.425 | 99,976.259 | 13.317 | 2,889.591 | 65,243.409 | 545,501.677 | 71,737.862 | 84,294.961 | 408.800 | 5.000 | 89.698 | 3.172 | 6.482 |
| std | 3.507 | 1,986,645.954 | 1,963,930.892 | 995,299.289 | 40.888 | 21,176.379 | 1,654,195.613 | 5,154,590.640 | 1,811,803.139 | 2,109,316.172 | 626.196 | 2.582 | 643.275 | 3.869 | 4.144 |
| min | 0.015 | -509.377 | -999.000 | 0.000 | -0.600 | -3.000 | -999.000 | -4.000 | -999.000 | -4.000 | 0.000 | 1.000 | 0.999 | 0.001 | 0.156 |
| 25% | 16.000 | 0.000 | 0.000 | 0.137 | 0.066 | -0.904 | -12.842 | 7.000 | 0.000 | 0.000 | 16.000 | 3.000 | 2.046 | 0.404 | 3.115 |
| 50% | 16.000 | 0.077 | 0.000 | 2.248 | 3.121 | 12.392 | 0.000 | 31.435 | 0.000 | 0.000 | 96.000 | 5.000 | 5.554 | 1.737 | 5.914 |
| 75% | 16.000 | 1.976 | 0.000 | 13.718 | 9.899 | 175.292 | 0.000 | 85.000 | 0.000 | 18.565 | 512.000 | 7.000 | 19.355 | 4.416 | 9.006 |
| max | 16.000 | 49,778,180.925 | 49,760,930.000 | 14,745,014.725 | 497.825 | 316,831.759 | 41,913,429.000 | 64,103,344.000 | 45,906,809.000 | 53,443,823.000 | 2,048.000 | 9.000 | 10,645.442 | 22.118 | 63.441 |
Filtramos el csv para eliminar ficheros que utilizan técnicas de compresión con pérdidas.
In [5]:
my_df = DF[(DF.Filename != 'WRF_India-LSD1.h5') & (DF.Filename != 'WRF_India-LSD2.h5') & (DF.Filename != 'WRF_India-LSD3.h5')]
Veamos cuantos conjuntos de datos tiene el fichero.
In [6]:
sets = my_df.drop_duplicates(subset=['DataSet', 'Table'])[cst.DESC_SET]
display(sets)
print('There are %d datasets' % (sets.shape[0]))
| DataSet | DType | Table | Chunk_Size | |
|---|---|---|---|---|
| 0 | /U | float32 | 0.000 | 16.000 |
| 85860 | /V | float32 | 0.000 | 16.000 |
| 150660 | /Grids/G1/precipAllObs | int32 | 0.000 | 0.738 |
| 152280 | /Grids/G1/surfPrecipLiqRateProb | float32 | 0.000 | 0.015 |
| 153900 | /Grids/G1/surfPrecipLiqRateUn | float32 | 0.000 | 0.015 |
| 155520 | /Grids/G1/surfPrecipTotRateDiurnalAllObs | int32 | 0.000 | 1.107 |
| 157140 | /Grids/G1/surfPrecipTotRateProb | float32 | 0.000 | 0.015 |
| 158760 | /Grids/G1/surfPrecipTotRateUn | float32 | 0.000 | 0.015 |
| 160380 | /Grids/G2/precipAllObs | int32 | 0.000 | 16.000 |
| 170100 | /Grids/G2/surfPrecipLiqRateProb | float32 | 0.000 | 5.889 |
| 171720 | /Grids/G2/surfPrecipLiqRateUn | float32 | 0.000 | 5.889 |
| 173340 | /Grids/G2/surfPrecipTotRateDiurnalAllObs | int32 | 0.000 | 16.000 |
| 187920 | /Grids/G2/surfPrecipTotRateProb | float32 | 0.000 | 5.889 |
| 189540 | /Grids/G2/surfPrecipTotRateUn | float32 | 0.000 | 5.889 |
| 191160 | /Grids/G1/precipLiqRate/count | int32 | 0.000 | 2.215 |
| 192780 | /Grids/G1/precipLiqRate/hist | int32 | 0.000 | 16.000 |
| 200880 | /Grids/G1/precipLiqRate/mean | float32 | 0.000 | 2.215 |
| 202500 | /Grids/G1/precipLiqRate/stdev | float32 | 0.000 | 2.215 |
| 204120 | /Grids/G1/precipLiqWaterContent/count | int32 | 0.000 | 2.215 |
| 205740 | /Grids/G1/precipLiqWaterContent/hist | int32 | 0.000 | 16.000 |
| 213840 | /Grids/G1/precipLiqWaterContent/mean | float32 | 0.000 | 2.215 |
| 215460 | /Grids/G1/precipLiqWaterContent/stdev | float32 | 0.000 | 2.215 |
| 217080 | /Grids/G1/precipTotDm/count | int32 | 0.000 | 2.215 |
| 218700 | /Grids/G1/precipTotDm/hist | int32 | 0.000 | 16.000 |
| 226800 | /Grids/G1/precipTotDm/mean | float32 | 0.000 | 2.215 |
| 228420 | /Grids/G1/precipTotDm/stdev | float32 | 0.000 | 2.215 |
| 230040 | /Grids/G1/precipTotLogNw/count | int32 | 0.000 | 2.215 |
| 231660 | /Grids/G1/precipTotLogNw/hist | int32 | 0.000 | 16.000 |
| 239760 | /Grids/G1/precipTotLogNw/mean | float32 | 0.000 | 2.215 |
| 241380 | /Grids/G1/precipTotLogNw/stdev | float32 | 0.000 | 2.215 |
| ... | ... | ... | ... | ... |
| 270540 | /Grids/G1/surfPrecipTotRateDiurnal/mean | float32 | 0.000 | 1.107 |
| 272160 | /Grids/G1/surfPrecipTotRateDiurnal/stdev | float32 | 0.000 | 1.107 |
| 273780 | /Grids/G2/precipLiqRate/count | int32 | 0.000 | 16.000 |
| 302940 | /Grids/G2/precipLiqRate/mean | float32 | 0.000 | 16.000 |
| 332100 | /Grids/G2/precipLiqRate/stdev | float32 | 0.000 | 16.000 |
| 361260 | /Grids/G2/precipLiqWaterContent/count | int32 | 0.000 | 16.000 |
| 390420 | /Grids/G2/precipLiqWaterContent/mean | float32 | 0.000 | 16.000 |
| 419580 | /Grids/G2/precipLiqWaterContent/stdev | float32 | 0.000 | 16.000 |
| 448740 | /Grids/G2/precipTotDm/count | int32 | 0.000 | 16.000 |
| 477900 | /Grids/G2/precipTotDm/mean | float32 | 0.000 | 16.000 |
| 507060 | /Grids/G2/precipTotDm/stdev | float32 | 0.000 | 16.000 |
| 536220 | /Grids/G2/precipTotLogNw/count | int32 | 0.000 | 16.000 |
| 565380 | /Grids/G2/precipTotLogNw/mean | float32 | 0.000 | 16.000 |
| 594540 | /Grids/G2/precipTotLogNw/stdev | float32 | 0.000 | 16.000 |
| 623700 | /Grids/G2/precipTotRate/count | int32 | 0.000 | 16.000 |
| 652860 | /Grids/G2/precipTotRate/mean | float32 | 0.000 | 16.000 |
| 682020 | /Grids/G2/precipTotRate/stdev | float32 | 0.000 | 16.000 |
| 711180 | /Grids/G2/precipTotWaterContent/count | int32 | 0.000 | 16.000 |
| 740340 | /Grids/G2/precipTotWaterContent/mean | float32 | 0.000 | 16.000 |
| 769500 | /Grids/G2/precipTotWaterContent/stdev | float32 | 0.000 | 16.000 |
| 798660 | /Grids/G2/surfPrecipTotRateDiurnal/count | int32 | 0.000 | 16.000 |
| 813240 | /Grids/G2/surfPrecipTotRateDiurnal/mean | float32 | 0.000 | 16.000 |
| 827820 | /Grids/G2/surfPrecipTotRateDiurnal/stdev | float32 | 0.000 | 16.000 |
| 842400 | /Tair_2m | float32 | 0.000 | 16.000 |
| 989820 | /msft/table.index | int64 | 1.000 | 5.087 |
| 991440 | /msft/table.values_block_0 | float64 | 1.000 | 16.000 |
| 1001160 | /msft/table.values_block_0 | float64 | 2.000 | 16.000 |
| 1010880 | /msft/table.values_block_1 | int64 | 1.000 | 5.087 |
| 1012500 | /msft/table.values_block_2 | |S49 | 1.000 | 16.000 |
| 1025460 | /msft/table.values_block_2 | |S49 | 2.000 | 16.000 |
69 rows × 4 columns
There are 69 datasets
Procedemos a mostrar un resumen de las caracterÃsticas extraÃdas de cada conjunto de datos.
In [7]:
for dataset in sets.drop_duplicates(subset=['DataSet'])['DataSet']:
set_info = sets[sets.DataSet == dataset]
print('SUMMARY')
print(set_info)
aux_set = my_df[my_df.DataSet == dataset].drop_duplicates(subset=['Chunk_Number'])
if aux_set.shape[0] > 1:
display(aux_set.describe()[cst.CHUNK_FEATURES])
else:
display(aux_set[cst.CHUNK_FEATURES])
SUMMARY DataSet DType Table Chunk_Size 0 /U float32 0.000 16.000
| Chunk_Size | Mean | Median | Sd | Skew | Kurt | Min | Max | Q1 | Q3 | |
|---|---|---|---|---|---|---|---|---|---|---|
| count | 53.000 | 53.000 | 53.000 | 53.000 | 53.000 | 53.000 | 53.000 | 53.000 | 53.000 | 53.000 |
| mean | 15.726 | 14.314 | 9.838 | 12.731 | 0.624 | -0.720 | -15.663 | 48.531 | 4.747 | 24.449 |
| std | 1.995 | 4.350 | 3.881 | 2.527 | 0.153 | 0.319 | 4.249 | 9.016 | 3.546 | 6.457 |
| min | 1.475 | 5.762 | 2.096 | 6.404 | 0.228 | -1.164 | -28.273 | 27.146 | -1.446 | 11.317 |
| 25% | 16.000 | 10.708 | 7.198 | 11.469 | 0.532 | -0.921 | -17.664 | 43.313 | 1.943 | 18.781 |
| 50% | 16.000 | 15.430 | 9.564 | 13.268 | 0.649 | -0.793 | -14.891 | 50.507 | 4.873 | 25.905 |
| 75% | 16.000 | 17.302 | 13.019 | 14.752 | 0.738 | -0.624 | -12.637 | 53.064 | 7.273 | 27.890 |
| max | 16.000 | 22.909 | 16.944 | 17.010 | 1.072 | 0.549 | -9.488 | 62.922 | 10.356 | 38.366 |
SUMMARY
DataSet DType Table Chunk_Size
85860 /V float32 0.000 16.000
| Chunk_Size | Mean | Median | Sd | Skew | Kurt | Min | Max | Q1 | Q3 | |
|---|---|---|---|---|---|---|---|---|---|---|
| count | 40.000 | 40.000 | 40.000 | 40.000 | 40.000 | 40.000 | 40.000 | 40.000 | 40.000 | 40.000 |
| mean | 15.900 | 2.073 | 1.526 | 4.976 | 0.311 | 0.301 | -16.644 | 23.037 | -1.197 | 4.881 |
| std | 0.631 | 2.517 | 1.696 | 2.391 | 0.510 | 0.742 | 4.047 | 9.672 | 1.472 | 3.369 |
| min | 12.009 | -1.626 | -1.256 | 2.474 | -0.600 | -0.370 | -29.640 | 13.443 | -4.887 | 0.552 |
| 25% | 16.000 | -0.240 | -0.088 | 3.414 | -0.031 | -0.162 | -19.447 | 16.708 | -2.397 | 2.357 |
| 50% | 16.000 | 1.886 | 1.721 | 4.303 | 0.253 | 0.240 | -16.237 | 20.683 | -0.907 | 4.500 |
| 75% | 16.000 | 3.743 | 2.851 | 5.930 | 0.644 | 0.484 | -13.344 | 25.571 | -0.204 | 6.422 |
| max | 16.000 | 9.091 | 4.968 | 14.419 | 1.748 | 4.040 | -11.483 | 56.161 | 1.253 | 15.373 |
SUMMARY
DataSet DType Table Chunk_Size
150660 /Grids/G1/precipAllObs int32 0.000 0.738
| Chunk_Size | Mean | Median | Sd | Skew | Kurt | Min | Max | Q1 | Q3 | |
|---|---|---|---|---|---|---|---|---|---|---|
| 150660 | 0.738 | 46,750.635 | 42,412.000 | 42,964.463 | 1.123 | 2.123 | 0.000 | 211,383.000 | 121.000 | 79,434.750 |
SUMMARY
DataSet DType Table Chunk_Size
152280 /Grids/G1/surfPrecipLiqRateProb float32 0.000 0.015
| Chunk_Size | Mean | Median | Sd | Skew | Kurt | Min | Max | Q1 | Q3 | |
|---|---|---|---|---|---|---|---|---|---|---|
| 152280 | 0.015 | 0.044 | 0.037 | 0.040 | 1.346 | 3.059 | 0.000 | 0.352 | 0.011 | 0.066 |
SUMMARY
DataSet DType Table Chunk_Size
153900 /Grids/G1/surfPrecipLiqRateUn float32 0.000 0.015
| Chunk_Size | Mean | Median | Sd | Skew | Kurt | Min | Max | Q1 | Q3 | |
|---|---|---|---|---|---|---|---|---|---|---|
| 153900 | 0.015 | 0.092 | 0.048 | 0.123 | 2.762 | 12.094 | 0.000 | 1.414 | 0.011 | 0.124 |
SUMMARY
DataSet DType Table Chunk_Size
155520 /Grids/G1/surfPrecipTotRateDiurnalAllObs int32 0.000 1.107
| Chunk_Size | Mean | Median | Sd | Skew | Kurt | Min | Max | Q1 | Q3 | |
|---|---|---|---|---|---|---|---|---|---|---|
| 155520 | 1.107 | 1,947.943 | 272.000 | 2,888.095 | 2.804 | 13.277 | 0.000 | 24,063.000 | 0.000 | 3,094.000 |
SUMMARY
DataSet DType Table Chunk_Size
157140 /Grids/G1/surfPrecipTotRateProb float32 0.000 0.015
| Chunk_Size | Mean | Median | Sd | Skew | Kurt | Min | Max | Q1 | Q3 | |
|---|---|---|---|---|---|---|---|---|---|---|
| 157140 | 0.015 | 0.050 | 0.043 | 0.040 | 1.218 | 2.721 | 0.000 | 0.352 | 0.018 | 0.072 |
SUMMARY
DataSet DType Table Chunk_Size
158760 /Grids/G1/surfPrecipTotRateUn float32 0.000 0.015
| Chunk_Size | Mean | Median | Sd | Skew | Kurt | Min | Max | Q1 | Q3 | |
|---|---|---|---|---|---|---|---|---|---|---|
| 158760 | 0.015 | 0.101 | 0.064 | 0.121 | 2.739 | 12.272 | 0.000 | 1.414 | 0.022 | 0.133 |
SUMMARY
DataSet DType Table Chunk_Size
160380 /Grids/G2/precipAllObs int32 0.000 16.000
| Chunk_Size | Mean | Median | Sd | Skew | Kurt | Min | Max | Q1 | Q3 | |
|---|---|---|---|---|---|---|---|---|---|---|
| count | 6.000 | 6.000 | 6.000 | 6.000 | 6.000 | 6.000 | 6.000 | 6.000 | 6.000 | 6.000 |
| mean | 15.703 | 183.354 | 173.500 | 107.464 | 2.317 | 9.421 | 0.000 | 910.000 | 116.167 | 222.333 |
| std | 0.727 | 6.775 | 10.710 | 1.604 | 0.023 | 0.173 | 0.000 | 0.000 | 5.742 | 4.502 |
| min | 14.219 | 176.954 | 163.000 | 105.856 | 2.292 | 9.218 | 0.000 | 910.000 | 111.000 | 218.000 |
| 25% | 16.000 | 177.709 | 164.250 | 106.084 | 2.296 | 9.273 | 0.000 | 910.000 | 111.500 | 218.500 |
| 50% | 16.000 | 181.741 | 171.500 | 107.211 | 2.316 | 9.426 | 0.000 | 910.000 | 114.500 | 221.500 |
| 75% | 16.000 | 187.916 | 181.750 | 108.644 | 2.338 | 9.577 | 0.000 | 910.000 | 119.750 | 225.250 |
| max | 16.000 | 193.347 | 188.000 | 109.676 | 2.342 | 9.605 | 0.000 | 910.000 | 125.000 | 229.000 |
SUMMARY
DataSet DType Table Chunk_Size
170100 /Grids/G2/surfPrecipLiqRateProb float32 0.000 5.889
| Chunk_Size | Mean | Median | Sd | Skew | Kurt | Min | Max | Q1 | Q3 | |
|---|---|---|---|---|---|---|---|---|---|---|
| 170100 | 5.889 | 0.045 | 0.009 | 0.074 | 2.804 | 12.031 | 0.000 | 1.000 | 0.000 | 0.063 |
SUMMARY
DataSet DType Table Chunk_Size
171720 /Grids/G2/surfPrecipLiqRateUn float32 0.000 5.889
| Chunk_Size | Mean | Median | Sd | Skew | Kurt | Min | Max | Q1 | Q3 | |
|---|---|---|---|---|---|---|---|---|---|---|
| 171720 | 5.889 | 0.094 | 0.004 | 0.337 | 12.404 | 321.944 | 0.000 | 26.186 | 0.000 | 0.051 |
SUMMARY
DataSet DType Table Chunk_Size
173340 /Grids/G2/surfPrecipTotRateDiurnalAllObs int32 0.000 16.000
| Chunk_Size | Mean | Median | Sd | Skew | Kurt | Min | Max | Q1 | Q3 | |
|---|---|---|---|---|---|---|---|---|---|---|
| count | 9.000 | 9.000 | 9.000 | 9.000 | 9.000 | 9.000 | 9.000 | 9.000 | 9.000 | 9.000 |
| mean | 15.703 | 7.629 | 0.000 | 12.705 | 1.798 | 4.317 | 0.000 | 102.222 | 0.000 | 13.778 |
| std | 0.891 | 1.237 | 0.000 | 0.843 | 0.792 | 4.134 | 0.000 | 17.683 | 0.000 | 8.059 |
| min | 13.328 | 5.467 | 0.000 | 11.541 | 0.728 | -1.091 | 0.000 | 65.000 | 0.000 | 0.000 |
| 25% | 16.000 | 7.239 | 0.000 | 11.730 | 1.005 | -0.012 | 0.000 | 93.000 | 0.000 | 14.000 |
| 50% | 16.000 | 8.051 | 0.000 | 12.935 | 1.725 | 4.011 | 0.000 | 113.000 | 0.000 | 18.000 |
| 75% | 16.000 | 8.505 | 0.000 | 13.343 | 2.259 | 6.763 | 0.000 | 113.000 | 0.000 | 19.000 |
| max | 16.000 | 9.073 | 0.000 | 13.710 | 2.875 | 9.907 | 0.000 | 114.000 | 0.000 | 20.000 |
SUMMARY
DataSet DType Table Chunk_Size
187920 /Grids/G2/surfPrecipTotRateProb float32 0.000 5.889
| Chunk_Size | Mean | Median | Sd | Skew | Kurt | Min | Max | Q1 | Q3 | |
|---|---|---|---|---|---|---|---|---|---|---|
| 187920 | 5.889 | 0.050 | 0.017 | 0.075 | 2.606 | 10.682 | 0.000 | 1.000 | 0.000 | 0.074 |
SUMMARY
DataSet DType Table Chunk_Size
189540 /Grids/G2/surfPrecipTotRateUn float32 0.000 5.889
| Chunk_Size | Mean | Median | Sd | Skew | Kurt | Min | Max | Q1 | Q3 | |
|---|---|---|---|---|---|---|---|---|---|---|
| 189540 | 5.889 | 0.103 | 0.011 | 0.338 | 12.253 | 317.113 | 0.000 | 26.186 | 0.000 | 0.074 |
SUMMARY
DataSet DType Table Chunk_Size
191160 /Grids/G1/precipLiqRate/count int32 0.000 2.215
| Chunk_Size | Mean | Median | Sd | Skew | Kurt | Min | Max | Q1 | Q3 | |
|---|---|---|---|---|---|---|---|---|---|---|
| 191160 | 2.215 | 290.349 | 0.000 | 1,105.965 | 6.631 | 63.209 | 0.000 | 27,765.000 | 0.000 | 0.000 |
SUMMARY
DataSet DType Table Chunk_Size
192780 /Grids/G1/precipLiqRate/hist int32 0.000 16.000
| Chunk_Size | Mean | Median | Sd | Skew | Kurt | Min | Max | Q1 | Q3 | |
|---|---|---|---|---|---|---|---|---|---|---|
| count | 5.000 | 5.000 | 5.000 | 5.000 | 5.000 | 5.000 | 5.000 | 5.000 | 5.000 | 5.000 |
| mean | 13.289 | 8.039 | 0.000 | 37.628 | 29.783 | 3,946.721 | 0.000 | 1,672.200 | 0.000 | 0.000 |
| std | 6.062 | 8.998 | 0.000 | 39.709 | 39.245 | 8,240.638 | 0.000 | 1,584.709 | 0.000 | 0.000 |
| min | 2.445 | 0.000 | 0.000 | 0.027 | 8.448 | 112.235 | 0.000 | 8.000 | 0.000 | 0.000 |
| 25% | 16.000 | 0.256 | 0.000 | 2.879 | 8.726 | 116.838 | 0.000 | 222.000 | 0.000 | 0.000 |
| 50% | 16.000 | 4.585 | 0.000 | 25.789 | 12.198 | 243.864 | 0.000 | 1,574.000 | 0.000 | 0.000 |
| 75% | 16.000 | 16.949 | 0.000 | 78.809 | 20.056 | 576.522 | 0.000 | 3,243.000 | 0.000 | 0.000 |
| max | 16.000 | 18.402 | 0.000 | 80.639 | 99.485 | 18,684.148 | 0.000 | 3,314.000 | 0.000 | 0.000 |
SUMMARY
DataSet DType Table Chunk_Size
200880 /Grids/G1/precipLiqRate/mean float32 0.000 2.215
| Chunk_Size | Mean | Median | Sd | Skew | Kurt | Min | Max | Q1 | Q3 | |
|---|---|---|---|---|---|---|---|---|---|---|
| 200880 | 2.215 | 0.456 | 0.000 | 1.532 | 8.146 | 210.120 | 0.000 | 122.311 | 0.000 | 0.000 |
SUMMARY
DataSet DType Table Chunk_Size
202500 /Grids/G1/precipLiqRate/stdev float32 0.000 2.215
| Chunk_Size | Mean | Median | Sd | Skew | Kurt | Min | Max | Q1 | Q3 | |
|---|---|---|---|---|---|---|---|---|---|---|
| 202500 | 2.215 | 0.650 | 0.000 | 2.098 | 4.997 | 32.385 | 0.000 | 43.932 | 0.000 | 0.000 |
SUMMARY
DataSet DType Table Chunk_Size
204120 /Grids/G1/precipLiqWaterContent/count int32 0.000 2.215
| Chunk_Size | Mean | Median | Sd | Skew | Kurt | Min | Max | Q1 | Q3 | |
|---|---|---|---|---|---|---|---|---|---|---|
| 204120 | 2.215 | 290.345 | 0.000 | 1,105.955 | 6.631 | 63.210 | 0.000 | 27,765.000 | 0.000 | 0.000 |
SUMMARY
DataSet DType Table Chunk_Size
205740 /Grids/G1/precipLiqWaterContent/hist int32 0.000 16.000
| Chunk_Size | Mean | Median | Sd | Skew | Kurt | Min | Max | Q1 | Q3 | |
|---|---|---|---|---|---|---|---|---|---|---|
| count | 5.000 | 5.000 | 5.000 | 5.000 | 5.000 | 5.000 | 5.000 | 5.000 | 5.000 | 5.000 |
| mean | 13.289 | 8.039 | 0.000 | 37.307 | 19.295 | 988.704 | 0.000 | 1,682.600 | 0.000 | 0.000 |
| std | 6.062 | 8.647 | 0.000 | 37.812 | 17.888 | 1,688.395 | 0.000 | 1,473.851 | 0.000 | 0.000 |
| min | 2.445 | 0.002 | 0.000 | 0.055 | 8.180 | 105.888 | 0.000 | 8.000 | 0.000 | 0.000 |
| 25% | 16.000 | 0.505 | 0.000 | 4.463 | 9.521 | 136.409 | 0.000 | 534.000 | 0.000 | 0.000 |
| 50% | 16.000 | 5.707 | 0.000 | 28.381 | 10.514 | 180.766 | 0.000 | 1,499.000 | 0.000 | 0.000 |
| 75% | 16.000 | 14.534 | 0.000 | 71.785 | 17.644 | 526.645 | 0.000 | 3,111.000 | 0.000 | 0.000 |
| max | 16.000 | 19.445 | 0.000 | 81.850 | 50.616 | 3,993.813 | 0.000 | 3,261.000 | 0.000 | 0.000 |
SUMMARY
DataSet DType Table Chunk_Size
213840 /Grids/G1/precipLiqWaterContent/mean float32 0.000 2.215
| Chunk_Size | Mean | Median | Sd | Skew | Kurt | Min | Max | Q1 | Q3 | |
|---|---|---|---|---|---|---|---|---|---|---|
| 213840 | 2.215 | 0.036 | 0.000 | 0.104 | 5.367 | 54.662 | 0.000 | 4.711 | 0.000 | 0.000 |
SUMMARY
DataSet DType Table Chunk_Size
215460 /Grids/G1/precipLiqWaterContent/stdev float32 0.000 2.215
| Chunk_Size | Mean | Median | Sd | Skew | Kurt | Min | Max | Q1 | Q3 | |
|---|---|---|---|---|---|---|---|---|---|---|
| 215460 | 2.215 | 0.044 | 0.000 | 0.127 | 4.180 | 23.454 | 0.000 | 3.249 | 0.000 | 0.000 |
SUMMARY
DataSet DType Table Chunk_Size
217080 /Grids/G1/precipTotDm/count int32 0.000 2.215
| Chunk_Size | Mean | Median | Sd | Skew | Kurt | Min | Max | Q1 | Q3 | |
|---|---|---|---|---|---|---|---|---|---|---|
| 217080 | 2.215 | 448.096 | 0.000 | 1,326.224 | 5.267 | 40.686 | 0.000 | 28,569.000 | 0.000 | 133.000 |
SUMMARY
DataSet DType Table Chunk_Size
218700 /Grids/G1/precipTotDm/hist int32 0.000 16.000
| Chunk_Size | Mean | Median | Sd | Skew | Kurt | Min | Max | Q1 | Q3 | |
|---|---|---|---|---|---|---|---|---|---|---|
| count | 5.000 | 5.000 | 5.000 | 5.000 | 5.000 | 5.000 | 5.000 | 5.000 | 5.000 | 5.000 |
| mean | 13.289 | 12.435 | 0.000 | 59.024 | 14.518 | 420.334 | 0.000 | 2,956.400 | 0.000 | 0.200 |
| std | 6.062 | 16.223 | 0.000 | 70.813 | 5.669 | 397.280 | 0.000 | 3,262.573 | 0.000 | 0.447 |
| min | 2.445 | 0.174 | 0.000 | 1.476 | 8.436 | 115.124 | 0.000 | 120.000 | 0.000 | 0.000 |
| 25% | 16.000 | 0.247 | 0.000 | 1.770 | 12.399 | 236.340 | 0.000 | 123.000 | 0.000 | 0.000 |
| 50% | 16.000 | 4.057 | 0.000 | 22.359 | 13.068 | 290.349 | 0.000 | 1,645.000 | 0.000 | 0.000 |
| 75% | 16.000 | 20.339 | 0.000 | 116.685 | 14.963 | 345.400 | 0.000 | 5,991.000 | 0.000 | 0.000 |
| max | 16.000 | 37.359 | 0.000 | 152.827 | 23.725 | 1,114.457 | 0.000 | 6,903.000 | 0.000 | 1.000 |
SUMMARY
DataSet DType Table Chunk_Size
226800 /Grids/G1/precipTotDm/mean float32 0.000 2.215
| Chunk_Size | Mean | Median | Sd | Skew | Kurt | Min | Max | Q1 | Q3 | |
|---|---|---|---|---|---|---|---|---|---|---|
| 226800 | 2.215 | 0.372 | 0.000 | 0.462 | 0.751 | -0.713 | 0.000 | 3.912 | 0.000 | 0.723 |
SUMMARY
DataSet DType Table Chunk_Size
228420 /Grids/G1/precipTotDm/stdev float32 0.000 2.215
| Chunk_Size | Mean | Median | Sd | Skew | Kurt | Min | Max | Q1 | Q3 | |
|---|---|---|---|---|---|---|---|---|---|---|
| 228420 | 2.215 | 0.088 | 0.000 | 0.145 | 4.611 | 99.472 | 0.000 | 7.870 | 0.000 | 0.152 |
SUMMARY
DataSet DType Table Chunk_Size
230040 /Grids/G1/precipTotLogNw/count int32 0.000 2.215
| Chunk_Size | Mean | Median | Sd | Skew | Kurt | Min | Max | Q1 | Q3 | |
|---|---|---|---|---|---|---|---|---|---|---|
| 230040 | 2.215 | 547.558 | 0.000 | 1,556.714 | 5.086 | 37.663 | 0.000 | 31,082.000 | 0.000 | 199.000 |
SUMMARY
DataSet DType Table Chunk_Size
231660 /Grids/G1/precipTotLogNw/hist int32 0.000 16.000
| Chunk_Size | Mean | Median | Sd | Skew | Kurt | Min | Max | Q1 | Q3 | |
|---|---|---|---|---|---|---|---|---|---|---|
| count | 5.000 | 5.000 | 5.000 | 5.000 | 5.000 | 5.000 | 5.000 | 5.000 | 5.000 | 5.000 |
| mean | 13.289 | 15.174 | 0.000 | 84.957 | 12.545 | 341.127 | 0.000 | 6,379.400 | 0.000 | 0.800 |
| std | 6.062 | 23.427 | 0.000 | 113.093 | 7.418 | 223.816 | 0.000 | 8,125.515 | 0.000 | 1.789 |
| min | 2.445 | 0.000 | 0.000 | 0.000 | 0.000 | -3.000 | 0.000 | 0.000 | 0.000 | 0.000 |
| 25% | 16.000 | 0.086 | 0.000 | 0.652 | 11.657 | 244.801 | 0.000 | 53.000 | 0.000 | 0.000 |
| 50% | 16.000 | 2.822 | 0.000 | 18.682 | 16.203 | 426.538 | 0.000 | 1,327.000 | 0.000 | 0.000 |
| 75% | 16.000 | 18.085 | 0.000 | 154.231 | 16.794 | 484.369 | 0.000 | 14,965.000 | 0.000 | 0.000 |
| max | 16.000 | 54.877 | 0.000 | 251.221 | 18.071 | 552.928 | 0.000 | 15,552.000 | 0.000 | 4.000 |
SUMMARY
DataSet DType Table Chunk_Size
239760 /Grids/G1/precipTotLogNw/mean float32 0.000 2.215
| Chunk_Size | Mean | Median | Sd | Skew | Kurt | Min | Max | Q1 | Q3 | |
|---|---|---|---|---|---|---|---|---|---|---|
| 239760 | 2.215 | 3.392 | 0.000 | 3.697 | 0.191 | -1.926 | 0.000 | 9.957 | 0.000 | 7.310 |
SUMMARY
DataSet DType Table Chunk_Size
241380 /Grids/G1/precipTotLogNw/stdev float32 0.000 2.215
| Chunk_Size | Mean | Median | Sd | Skew | Kurt | Min | Max | Q1 | Q3 | |
|---|---|---|---|---|---|---|---|---|---|---|
| 241380 | 2.215 | 0.129 | 0.000 | 0.174 | 1.084 | 0.046 | 0.000 | 1.303 | 0.000 | 0.246 |
SUMMARY
DataSet DType Table Chunk_Size
243000 /Grids/G1/precipTotRate/count int32 0.000 2.215
| Chunk_Size | Mean | Median | Sd | Skew | Kurt | Min | Max | Q1 | Q3 | |
|---|---|---|---|---|---|---|---|---|---|---|
| 243000 | 2.215 | 448.460 | 0.000 | 1,326.900 | 5.265 | 40.637 | 0.000 | 28,569.000 | 0.000 | 134.000 |
SUMMARY
DataSet DType Table Chunk_Size
244620 /Grids/G1/precipTotRate/hist int32 0.000 16.000
| Chunk_Size | Mean | Median | Sd | Skew | Kurt | Min | Max | Q1 | Q3 | |
|---|---|---|---|---|---|---|---|---|---|---|
| count | 5.000 | 5.000 | 5.000 | 5.000 | 5.000 | 5.000 | 5.000 | 5.000 | 5.000 | 5.000 |
| mean | 13.289 | 12.416 | 0.000 | 48.134 | 18.219 | 903.809 | 0.000 | 1,956.400 | 0.000 | 1.600 |
| std | 6.062 | 16.201 | 0.000 | 55.736 | 16.732 | 1,561.170 | 0.000 | 2,024.067 | 0.000 | 3.578 |
| min | 2.445 | 0.001 | 0.000 | 0.045 | 6.722 | 70.836 | 0.000 | 7.000 | 0.000 | 0.000 |
| 25% | 16.000 | 0.342 | 0.000 | 3.142 | 8.825 | 118.443 | 0.000 | 222.000 | 0.000 | 0.000 |
| 50% | 16.000 | 6.154 | 0.000 | 28.635 | 10.666 | 188.103 | 0.000 | 1,574.000 | 0.000 | 0.000 |
| 75% | 16.000 | 16.885 | 0.000 | 78.235 | 17.648 | 458.031 | 0.000 | 3,226.000 | 0.000 | 0.000 |
| max | 16.000 | 38.698 | 0.000 | 130.615 | 47.237 | 3,683.632 | 0.000 | 4,753.000 | 0.000 | 8.000 |
SUMMARY
DataSet DType Table Chunk_Size
252720 /Grids/G1/precipTotRate/mean float32 0.000 2.215
| Chunk_Size | Mean | Median | Sd | Skew | Kurt | Min | Max | Q1 | Q3 | |
|---|---|---|---|---|---|---|---|---|---|---|
| 252720 | 2.215 | 0.958 | 0.000 | 1.966 | 7.487 | 176.814 | 0.000 | 122.311 | 0.000 | 1.304 |
SUMMARY
DataSet DType Table Chunk_Size
254340 /Grids/G1/precipTotRate/stdev float32 0.000 2.215
| Chunk_Size | Mean | Median | Sd | Skew | Kurt | Min | Max | Q1 | Q3 | |
|---|---|---|---|---|---|---|---|---|---|---|
| 254340 | 2.215 | 1.129 | 0.000 | 2.648 | 4.428 | 34.107 | 0.000 | 83.595 | 0.000 | 0.935 |
SUMMARY
DataSet DType Table Chunk_Size
255960 /Grids/G1/precipTotWaterContent/count int32 0.000 2.215
| Chunk_Size | Mean | Median | Sd | Skew | Kurt | Min | Max | Q1 | Q3 | |
|---|---|---|---|---|---|---|---|---|---|---|
| 255960 | 2.215 | 448.131 | 0.000 | 1,326.512 | 5.267 | 40.665 | 0.000 | 28,568.000 | 0.000 | 133.000 |
SUMMARY
DataSet DType Table Chunk_Size
257580 /Grids/G1/precipTotWaterContent/hist int32 0.000 16.000
| Chunk_Size | Mean | Median | Sd | Skew | Kurt | Min | Max | Q1 | Q3 | |
|---|---|---|---|---|---|---|---|---|---|---|
| count | 5.000 | 5.000 | 5.000 | 5.000 | 5.000 | 5.000 | 5.000 | 5.000 | 5.000 | 5.000 |
| mean | 13.289 | 12.451 | 0.000 | 52.148 | 11.020 | 208.264 | 0.000 | 2,301.600 | 0.000 | 1.000 |
| std | 6.062 | 11.237 | 0.000 | 42.869 | 3.464 | 128.644 | 0.000 | 1,730.232 | 0.000 | 1.732 |
| min | 2.445 | 0.261 | 0.000 | 1.994 | 7.174 | 79.899 | 0.000 | 108.000 | 0.000 | 0.000 |
| 25% | 16.000 | 1.962 | 0.000 | 10.808 | 8.621 | 117.807 | 0.000 | 897.000 | 0.000 | 0.000 |
| 50% | 16.000 | 12.938 | 0.000 | 67.805 | 10.065 | 150.893 | 0.000 | 3,050.000 | 0.000 | 0.000 |
| 75% | 16.000 | 23.513 | 0.000 | 88.349 | 14.069 | 339.163 | 0.000 | 3,193.000 | 0.000 | 1.000 |
| max | 16.000 | 23.580 | 0.000 | 91.784 | 15.173 | 353.560 | 0.000 | 4,260.000 | 0.000 | 4.000 |
SUMMARY
DataSet DType Table Chunk_Size
265680 /Grids/G1/precipTotWaterContent/mean float32 0.000 2.215
| Chunk_Size | Mean | Median | Sd | Skew | Kurt | Min | Max | Q1 | Q3 | |
|---|---|---|---|---|---|---|---|---|---|---|
| 265680 | 2.215 | 0.196 | 0.000 | 0.344 | 3.503 | 29.819 | 0.000 | 9.445 | 0.000 | 0.341 |
SUMMARY
DataSet DType Table Chunk_Size
267300 /Grids/G1/precipTotWaterContent/stdev float32 0.000 2.215
| Chunk_Size | Mean | Median | Sd | Skew | Kurt | Min | Max | Q1 | Q3 | |
|---|---|---|---|---|---|---|---|---|---|---|
| 267300 | 2.215 | 0.163 | 0.000 | 0.329 | 3.201 | 12.698 | 0.000 | 4.015 | 0.000 | 0.205 |
SUMMARY
DataSet DType Table Chunk_Size
268920 /Grids/G1/surfPrecipTotRateDiurnal/count int32 0.000 1.107
| Chunk_Size | Mean | Median | Sd | Skew | Kurt | Min | Max | Q1 | Q3 | |
|---|---|---|---|---|---|---|---|---|---|---|
| 268920 | 1.107 | 97.688 | 0.000 | 285.385 | 5.308 | 39.019 | 0.000 | 5,666.000 | 0.000 | 40.000 |
SUMMARY
DataSet DType Table Chunk_Size
270540 /Grids/G1/surfPrecipTotRateDiurnal/mean float32 0.000 1.107
| Chunk_Size | Mean | Median | Sd | Skew | Kurt | Min | Max | Q1 | Q3 | |
|---|---|---|---|---|---|---|---|---|---|---|
| 270540 | 1.107 | 0.591 | 0.000 | 1.355 | 16.012 | 879.167 | 0.000 | 128.023 | 0.000 | 0.796 |
SUMMARY
DataSet DType Table Chunk_Size
272160 /Grids/G1/surfPrecipTotRateDiurnal/stdev float32 0.000 1.107
| Chunk_Size | Mean | Median | Sd | Skew | Kurt | Min | Max | Q1 | Q3 | |
|---|---|---|---|---|---|---|---|---|---|---|
| 272160 | 1.107 | 0.771 | 0.000 | 2.007 | 6.019 | 90.559 | 0.000 | 91.046 | 0.000 | 0.536 |
SUMMARY
DataSet DType Table Chunk_Size
273780 /Grids/G2/precipLiqRate/count int32 0.000 16.000
| Chunk_Size | Mean | Median | Sd | Skew | Kurt | Min | Max | Q1 | Q3 | |
|---|---|---|---|---|---|---|---|---|---|---|
| count | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 |
| mean | 15.703 | 1.116 | 0.000 | 2.974 | 6.481 | 180.121 | 0.000 | 69.889 | 0.000 | 0.556 |
| std | 1.260 | 1.730 | 0.000 | 3.775 | 10.321 | 501.302 | 0.000 | 69.628 | 0.000 | 1.542 |
| min | 10.656 | 0.000 | 0.000 | 0.000 | 0.000 | -3.000 | 0.000 | 0.000 | 0.000 | 0.000 |
| 25% | 16.000 | 0.000 | 0.000 | 0.000 | 0.000 | -3.000 | 0.000 | 0.000 | 0.000 | 0.000 |
| 50% | 16.000 | 0.101 | 0.000 | 0.951 | 3.409 | 15.641 | 0.000 | 60.000 | 0.000 | 0.000 |
| 75% | 16.000 | 1.624 | 0.000 | 5.388 | 6.060 | 49.034 | 0.000 | 143.250 | 0.000 | 0.000 |
| max | 16.000 | 5.748 | 0.000 | 11.004 | 41.152 | 2,111.449 | 0.000 | 175.000 | 0.000 | 6.000 |
SUMMARY
DataSet DType Table Chunk_Size
302940 /Grids/G2/precipLiqRate/mean float32 0.000 16.000
| Chunk_Size | Mean | Median | Sd | Skew | Kurt | Min | Max | Q1 | Q3 | |
|---|---|---|---|---|---|---|---|---|---|---|
| count | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 |
| mean | 15.703 | 0.187 | 0.000 | 0.686 | 14.115 | 1,546.143 | 0.000 | 77.343 | 0.000 | 0.086 |
| std | 1.260 | 0.286 | 0.000 | 0.902 | 25.548 | 5,105.949 | 0.000 | 96.388 | 0.000 | 0.203 |
| min | 10.656 | 0.000 | 0.000 | 0.000 | 0.000 | -3.000 | 0.000 | 0.000 | 0.000 | 0.000 |
| 25% | 16.000 | 0.000 | 0.000 | 0.000 | 0.000 | -3.000 | 0.000 | 0.000 | 0.000 | 0.000 |
| 50% | 16.000 | 0.017 | 0.000 | 0.218 | 6.596 | 91.487 | 0.000 | 28.947 | 0.000 | 0.000 |
| 75% | 16.000 | 0.296 | 0.000 | 1.023 | 16.177 | 715.496 | 0.000 | 157.397 | 0.000 | 0.000 |
| max | 16.000 | 0.989 | 0.000 | 2.996 | 108.763 | 21,889.348 | 0.000 | 295.298 | 0.000 | 0.647 |
SUMMARY
DataSet DType Table Chunk_Size
332100 /Grids/G2/precipLiqRate/stdev float32 0.000 16.000
| Chunk_Size | Mean | Median | Sd | Skew | Kurt | Min | Max | Q1 | Q3 | |
|---|---|---|---|---|---|---|---|---|---|---|
| count | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 |
| mean | 15.703 | 0.140 | 0.000 | 0.589 | 16.052 | 2,989.569 | 0.000 | 53.402 | 0.000 | 0.023 |
| std | 1.260 | 0.213 | 0.000 | 0.755 | 31.771 | 10,550.917 | 0.000 | 56.937 | 0.000 | 0.074 |
| min | 10.656 | 0.000 | 0.000 | 0.000 | 0.000 | -3.000 | 0.000 | 0.000 | 0.000 | 0.000 |
| 25% | 16.000 | 0.000 | 0.000 | 0.000 | 0.000 | -3.000 | 0.000 | 0.000 | 0.000 | 0.000 |
| 50% | 16.000 | 0.011 | 0.000 | 0.162 | 6.036 | 64.670 | 0.000 | 31.550 | 0.000 | 0.000 |
| 75% | 16.000 | 0.203 | 0.000 | 1.004 | 14.155 | 640.694 | 0.000 | 113.004 | 0.000 | 0.000 |
| max | 16.000 | 0.660 | 0.000 | 2.248 | 135.611 | 45,023.028 | 0.000 | 138.487 | 0.000 | 0.298 |
SUMMARY
DataSet DType Table Chunk_Size
361260 /Grids/G2/precipLiqWaterContent/count int32 0.000 16.000
| Chunk_Size | Mean | Median | Sd | Skew | Kurt | Min | Max | Q1 | Q3 | |
|---|---|---|---|---|---|---|---|---|---|---|
| count | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 |
| mean | 15.703 | 1.116 | 0.000 | 2.974 | 6.481 | 180.082 | 0.000 | 69.889 | 0.000 | 0.556 |
| std | 1.260 | 1.730 | 0.000 | 3.774 | 10.320 | 501.151 | 0.000 | 69.628 | 0.000 | 1.542 |
| min | 10.656 | 0.000 | 0.000 | 0.000 | 0.000 | -3.000 | 0.000 | 0.000 | 0.000 | 0.000 |
| 25% | 16.000 | 0.000 | 0.000 | 0.000 | 0.000 | -3.000 | 0.000 | 0.000 | 0.000 | 0.000 |
| 50% | 16.000 | 0.101 | 0.000 | 0.951 | 3.409 | 15.641 | 0.000 | 60.000 | 0.000 | 0.000 |
| 75% | 16.000 | 1.624 | 0.000 | 5.388 | 6.060 | 49.035 | 0.000 | 143.250 | 0.000 | 0.000 |
| max | 16.000 | 5.748 | 0.000 | 11.004 | 41.147 | 2,110.792 | 0.000 | 175.000 | 0.000 | 6.000 |
SUMMARY
DataSet DType Table Chunk_Size
390420 /Grids/G2/precipLiqWaterContent/mean float32 0.000 16.000
| Chunk_Size | Mean | Median | Sd | Skew | Kurt | Min | Max | Q1 | Q3 | |
|---|---|---|---|---|---|---|---|---|---|---|
| count | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 |
| mean | 15.703 | 0.015 | 0.000 | 0.048 | 12.106 | 1,176.595 | 0.000 | 4.379 | 0.000 | 0.008 |
| std | 1.260 | 0.021 | 0.000 | 0.058 | 22.161 | 3,893.149 | 0.000 | 4.065 | 0.000 | 0.019 |
| min | 10.656 | 0.000 | 0.000 | 0.000 | 0.000 | -3.000 | 0.000 | 0.000 | 0.000 | 0.000 |
| 25% | 16.000 | 0.000 | 0.000 | 0.000 | 0.000 | -3.000 | 0.000 | 0.000 | 0.000 | 0.000 |
| 50% | 16.000 | 0.003 | 0.000 | 0.035 | 5.869 | 80.234 | 0.000 | 4.407 | 0.000 | 0.000 |
| 75% | 16.000 | 0.025 | 0.000 | 0.060 | 12.625 | 639.552 | 0.000 | 8.588 | 0.000 | 0.000 |
| max | 16.000 | 0.067 | 0.000 | 0.175 | 94.256 | 16,687.977 | 0.000 | 9.696 | 0.000 | 0.058 |
SUMMARY
DataSet DType Table Chunk_Size
419580 /Grids/G2/precipLiqWaterContent/stdev float32 0.000 16.000
| Chunk_Size | Mean | Median | Sd | Skew | Kurt | Min | Max | Q1 | Q3 | |
|---|---|---|---|---|---|---|---|---|---|---|
| count | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 |
| mean | 15.703 | 0.010 | 0.000 | 0.038 | 11.486 | 835.252 | 0.000 | 2.259 | 0.000 | 0.002 |
| std | 1.260 | 0.014 | 0.000 | 0.044 | 20.344 | 2,660.245 | 0.000 | 1.947 | 0.000 | 0.006 |
| min | 10.656 | 0.000 | 0.000 | 0.000 | 0.000 | -3.000 | 0.000 | 0.000 | 0.000 | 0.000 |
| 25% | 16.000 | 0.000 | 0.000 | 0.000 | 0.000 | -3.000 | 0.000 | 0.000 | 0.000 | 0.000 |
| 50% | 16.000 | 0.002 | 0.000 | 0.025 | 5.575 | 55.408 | 0.000 | 2.821 | 0.000 | 0.000 |
| 75% | 16.000 | 0.015 | 0.000 | 0.052 | 9.562 | 141.844 | 0.000 | 4.013 | 0.000 | 0.000 |
| max | 16.000 | 0.040 | 0.000 | 0.118 | 85.130 | 11,360.029 | 0.000 | 4.575 | 0.000 | 0.025 |
SUMMARY
DataSet DType Table Chunk_Size
448740 /Grids/G2/precipTotDm/count int32 0.000 16.000
| Chunk_Size | Mean | Median | Sd | Skew | Kurt | Min | Max | Q1 | Q3 | |
|---|---|---|---|---|---|---|---|---|---|---|
| count | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 |
| mean | 15.703 | 1.723 | 0.056 | 4.212 | 14.227 | 746.306 | 0.000 | 96.222 | 0.000 | 1.556 |
| std | 1.260 | 2.224 | 0.236 | 3.901 | 16.397 | 1,691.575 | 0.000 | 53.290 | 0.000 | 2.975 |
| min | 10.656 | 0.001 | 0.000 | 0.042 | 2.541 | 9.251 | 0.000 | 11.000 | 0.000 | 0.000 |
| 25% | 16.000 | 0.153 | 0.000 | 1.139 | 4.039 | 22.405 | 0.000 | 55.500 | 0.000 | 0.000 |
| 50% | 16.000 | 0.641 | 0.000 | 2.752 | 6.444 | 56.111 | 0.000 | 77.500 | 0.000 | 0.000 |
| 75% | 16.000 | 1.753 | 0.000 | 6.213 | 12.021 | 191.830 | 0.000 | 147.500 | 0.000 | 1.500 |
| max | 16.000 | 7.195 | 1.000 | 11.825 | 57.967 | 6,928.863 | 0.000 | 175.000 | 0.000 | 10.000 |
SUMMARY
DataSet DType Table Chunk_Size
477900 /Grids/G2/precipTotDm/mean float32 0.000 16.000
| Chunk_Size | Mean | Median | Sd | Skew | Kurt | Min | Max | Q1 | Q3 | |
|---|---|---|---|---|---|---|---|---|---|---|
| count | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 |
| mean | 15.703 | 0.162 | 0.040 | 0.274 | 6.114 | 101.237 | 0.000 | 3.361 | 0.000 | 0.249 |
| std | 1.260 | 0.182 | 0.170 | 0.191 | 8.065 | 249.992 | 0.000 | 0.958 | 0.000 | 0.417 |
| min | 10.656 | 0.001 | 0.000 | 0.020 | 0.157 | -1.389 | 0.000 | 1.158 | 0.000 | 0.000 |
| 25% | 16.000 | 0.025 | 0.000 | 0.126 | 1.196 | 0.233 | 0.000 | 3.235 | 0.000 | 0.000 |
| 50% | 16.000 | 0.089 | 0.000 | 0.242 | 3.015 | 8.887 | 0.000 | 3.856 | 0.000 | 0.000 |
| 75% | 16.000 | 0.301 | 0.000 | 0.445 | 5.547 | 32.356 | 0.000 | 3.959 | 0.000 | 0.575 |
| max | 16.000 | 0.564 | 0.720 | 0.578 | 31.921 | 1,046.805 | 0.000 | 3.999 | 0.000 | 1.032 |
SUMMARY
DataSet DType Table Chunk_Size
507060 /Grids/G2/precipTotDm/stdev float32 0.000 16.000
| Chunk_Size | Mean | Median | Sd | Skew | Kurt | Min | Max | Q1 | Q3 | |
|---|---|---|---|---|---|---|---|---|---|---|
| count | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 |
| mean | 15.703 | 0.023 | 0.000 | 0.055 | 24.031 | 4,421.390 | 0.000 | 1.480 | 0.000 | 0.025 |
| std | 1.260 | 0.028 | 0.000 | 0.044 | 56.508 | 17,067.517 | 0.000 | 0.388 | 0.000 | 0.051 |
| min | 10.656 | 0.000 | 0.000 | 0.001 | 1.690 | 3.841 | 0.000 | 0.239 | 0.000 | 0.000 |
| 25% | 16.000 | 0.003 | 0.000 | 0.019 | 2.913 | 12.174 | 0.000 | 1.543 | 0.000 | 0.000 |
| 50% | 16.000 | 0.011 | 0.000 | 0.046 | 6.459 | 61.163 | 0.000 | 1.582 | 0.000 | 0.000 |
| 75% | 16.000 | 0.041 | 0.000 | 0.089 | 12.456 | 314.355 | 0.000 | 1.676 | 0.000 | 0.012 |
| max | 16.000 | 0.093 | 0.000 | 0.130 | 245.611 | 72,752.224 | 0.000 | 1.856 | 0.000 | 0.169 |
SUMMARY
DataSet DType Table Chunk_Size
536220 /Grids/G2/precipTotLogNw/count int32 0.000 16.000
| Chunk_Size | Mean | Median | Sd | Skew | Kurt | Min | Max | Q1 | Q3 | |
|---|---|---|---|---|---|---|---|---|---|---|
| count | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 |
| mean | 15.703 | 2.106 | 0.167 | 4.762 | 12.146 | 418.776 | 0.000 | 105.944 | 0.000 | 2.056 |
| std | 1.260 | 2.671 | 0.514 | 4.371 | 12.597 | 701.827 | 0.000 | 59.545 | 0.000 | 3.811 |
| min | 10.656 | 0.004 | 0.000 | 0.078 | 2.413 | 8.330 | 0.000 | 11.000 | 0.000 | 0.000 |
| 25% | 16.000 | 0.157 | 0.000 | 1.162 | 3.538 | 16.850 | 0.000 | 55.750 | 0.000 | 0.000 |
| 50% | 16.000 | 1.268 | 0.000 | 3.507 | 5.710 | 44.225 | 0.000 | 85.000 | 0.000 | 0.000 |
| 75% | 16.000 | 2.011 | 0.000 | 7.067 | 11.787 | 184.479 | 0.000 | 155.000 | 0.000 | 2.000 |
| max | 16.000 | 8.564 | 2.000 | 13.245 | 37.539 | 1,884.219 | 0.000 | 193.000 | 0.000 | 13.000 |
SUMMARY
DataSet DType Table Chunk_Size
565380 /Grids/G2/precipTotLogNw/mean float32 0.000 16.000
| Chunk_Size | Mean | Median | Sd | Skew | Kurt | Min | Max | Q1 | Q3 | |
|---|---|---|---|---|---|---|---|---|---|---|
| count | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 |
| mean | 15.703 | 1.445 | 0.735 | 2.273 | 4.434 | 46.850 | 0.000 | 9.877 | 0.000 | 2.291 |
| std | 1.260 | 1.403 | 2.140 | 1.147 | 5.551 | 93.752 | 0.000 | 0.217 | 0.000 | 3.334 |
| min | 10.656 | 0.024 | 0.000 | 0.430 | -0.430 | -1.953 | 0.000 | 9.178 | 0.000 | 0.000 |
| 25% | 16.000 | 0.293 | 0.000 | 1.426 | 0.539 | -1.676 | 0.000 | 9.921 | 0.000 | 0.000 |
| 50% | 16.000 | 1.003 | 0.000 | 2.507 | 2.104 | 2.454 | 0.000 | 9.967 | 0.000 | 0.000 |
| 75% | 16.000 | 2.652 | 0.000 | 3.397 | 4.854 | 21.648 | 0.000 | 9.984 | 0.000 | 6.741 |
| max | 16.000 | 4.211 | 6.647 | 3.568 | 18.154 | 327.643 | 0.000 | 9.998 | 0.000 | 7.043 |
SUMMARY
DataSet DType Table Chunk_Size
594540 /Grids/G2/precipTotLogNw/stdev float32 0.000 16.000
| Chunk_Size | Mean | Median | Sd | Skew | Kurt | Min | Max | Q1 | Q3 | |
|---|---|---|---|---|---|---|---|---|---|---|
| count | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 |
| mean | 15.703 | 0.033 | 0.003 | 0.075 | 15.739 | 1,878.335 | 0.000 | 1.474 | 0.000 | 0.038 |
| std | 1.260 | 0.035 | 0.013 | 0.043 | 31.126 | 7,120.602 | 0.000 | 0.206 | 0.000 | 0.068 |
| min | 10.656 | 0.000 | 0.000 | 0.002 | 1.284 | 1.706 | 0.000 | 0.865 | 0.000 | 0.000 |
| 25% | 16.000 | 0.005 | 0.000 | 0.040 | 3.015 | 10.717 | 0.000 | 1.371 | 0.000 | 0.000 |
| 50% | 16.000 | 0.023 | 0.000 | 0.080 | 4.557 | 26.191 | 0.000 | 1.509 | 0.000 | 0.000 |
| 75% | 16.000 | 0.043 | 0.000 | 0.106 | 9.583 | 107.163 | 0.000 | 1.636 | 0.000 | 0.041 |
| max | 16.000 | 0.113 | 0.054 | 0.139 | 134.109 | 30,370.566 | 0.000 | 1.709 | 0.000 | 0.199 |
SUMMARY
DataSet DType Table Chunk_Size
623700 /Grids/G2/precipTotRate/count int32 0.000 16.000
| Chunk_Size | Mean | Median | Sd | Skew | Kurt | Min | Max | Q1 | Q3 | |
|---|---|---|---|---|---|---|---|---|---|---|
| count | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 |
| mean | 15.703 | 1.724 | 0.056 | 4.216 | 14.224 | 746.030 | 0.000 | 96.222 | 0.000 | 1.556 |
| std | 1.260 | 2.226 | 0.236 | 3.904 | 16.394 | 1,691.418 | 0.000 | 53.290 | 0.000 | 2.975 |
| min | 10.656 | 0.001 | 0.000 | 0.042 | 2.540 | 9.240 | 0.000 | 11.000 | 0.000 | 0.000 |
| 25% | 16.000 | 0.153 | 0.000 | 1.141 | 4.039 | 22.403 | 0.000 | 55.500 | 0.000 | 0.000 |
| 50% | 16.000 | 0.641 | 0.000 | 2.754 | 6.441 | 56.051 | 0.000 | 77.500 | 0.000 | 0.000 |
| 75% | 16.000 | 1.755 | 0.000 | 6.222 | 12.018 | 191.738 | 0.000 | 147.500 | 0.000 | 1.500 |
| max | 16.000 | 7.200 | 1.000 | 11.832 | 57.967 | 6,928.863 | 0.000 | 175.000 | 0.000 | 10.000 |
SUMMARY
DataSet DType Table Chunk_Size
652860 /Grids/G2/precipTotRate/mean float32 0.000 16.000
| Chunk_Size | Mean | Median | Sd | Skew | Kurt | Min | Max | Q1 | Q3 | |
|---|---|---|---|---|---|---|---|---|---|---|
| count | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 |
| mean | 15.703 | 0.313 | 0.019 | 1.063 | 43.524 | 11,967.816 | 0.000 | 160.361 | 0.000 | 0.222 |
| std | 1.260 | 0.330 | 0.080 | 0.841 | 55.596 | 24,314.865 | 0.000 | 72.186 | 0.000 | 0.373 |
| min | 10.656 | 0.001 | 0.000 | 0.080 | 4.918 | 52.856 | 0.000 | 41.717 | 0.000 | 0.000 |
| 25% | 16.000 | 0.075 | 0.000 | 0.450 | 8.727 | 201.248 | 0.000 | 96.891 | 0.000 | 0.000 |
| 50% | 16.000 | 0.207 | 0.000 | 0.899 | 19.486 | 1,210.433 | 0.000 | 193.685 | 0.000 | 0.000 |
| 75% | 16.000 | 0.470 | 0.000 | 1.443 | 58.571 | 9,298.809 | 0.000 | 209.951 | 0.000 | 0.511 |
| max | 16.000 | 1.116 | 0.341 | 3.098 | 199.003 | 85,613.265 | 0.000 | 242.941 | 0.000 | 1.005 |
SUMMARY
DataSet DType Table Chunk_Size
682020 /Grids/G2/precipTotRate/stdev float32 0.000 16.000
| Chunk_Size | Mean | Median | Sd | Skew | Kurt | Min | Max | Q1 | Q3 | |
|---|---|---|---|---|---|---|---|---|---|---|
| count | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 |
| mean | 15.703 | 0.200 | 0.000 | 0.886 | 78.894 | 36,212.459 | 0.000 | 100.504 | 0.000 | 0.072 |
| std | 1.260 | 0.232 | 0.000 | 0.711 | 133.720 | 89,119.753 | 0.000 | 34.867 | 0.000 | 0.146 |
| min | 10.656 | 0.000 | 0.000 | 0.011 | 5.560 | 51.032 | 0.000 | 9.596 | 0.000 | 0.000 |
| 25% | 16.000 | 0.025 | 0.000 | 0.267 | 10.123 | 198.872 | 0.000 | 82.113 | 0.000 | 0.000 |
| 50% | 16.000 | 0.119 | 0.000 | 0.659 | 21.039 | 904.843 | 0.000 | 110.162 | 0.000 | 0.000 |
| 75% | 16.000 | 0.264 | 0.000 | 1.284 | 82.732 | 14,026.322 | 0.000 | 125.372 | 0.000 | 0.028 |
| max | 16.000 | 0.716 | 0.000 | 2.284 | 497.825 | 316,831.759 | 0.000 | 147.276 | 0.000 | 0.511 |
SUMMARY
DataSet DType Table Chunk_Size
711180 /Grids/G2/precipTotWaterContent/count int32 0.000 16.000
| Chunk_Size | Mean | Median | Sd | Skew | Kurt | Min | Max | Q1 | Q3 | |
|---|---|---|---|---|---|---|---|---|---|---|
| count | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 |
| mean | 15.703 | 1.723 | 0.056 | 4.209 | 14.183 | 743.937 | 0.000 | 96.222 | 0.000 | 1.556 |
| std | 1.260 | 2.226 | 0.236 | 3.905 | 16.353 | 1,691.146 | 0.000 | 53.290 | 0.000 | 2.975 |
| min | 10.656 | 0.001 | 0.000 | 0.042 | 2.540 | 9.240 | 0.000 | 11.000 | 0.000 | 0.000 |
| 25% | 16.000 | 0.152 | 0.000 | 1.129 | 4.038 | 22.407 | 0.000 | 55.500 | 0.000 | 0.000 |
| 50% | 16.000 | 0.639 | 0.000 | 2.744 | 6.402 | 55.303 | 0.000 | 77.500 | 0.000 | 0.000 |
| 75% | 16.000 | 1.755 | 0.000 | 6.217 | 11.993 | 191.007 | 0.000 | 147.500 | 0.000 | 1.500 |
| max | 16.000 | 7.200 | 1.000 | 11.832 | 57.993 | 6,934.303 | 0.000 | 175.000 | 0.000 | 10.000 |
SUMMARY
DataSet DType Table Chunk_Size
740340 /Grids/G2/precipTotWaterContent/mean float32 0.000 16.000
| Chunk_Size | Mean | Median | Sd | Skew | Kurt | Min | Max | Q1 | Q3 | |
|---|---|---|---|---|---|---|---|---|---|---|
| count | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 |
| mean | 15.703 | 0.055 | 0.002 | 0.175 | 18.521 | 1,933.941 | 0.000 | 9.444 | 0.000 | 0.029 |
| std | 1.260 | 0.044 | 0.007 | 0.106 | 29.865 | 6,341.308 | 0.000 | 0.979 | 0.000 | 0.052 |
| min | 10.656 | 0.001 | 0.000 | 0.024 | 3.608 | 30.147 | 0.000 | 5.999 | 0.000 | 0.000 |
| 25% | 16.000 | 0.012 | 0.000 | 0.081 | 5.031 | 59.039 | 0.000 | 9.460 | 0.000 | 0.000 |
| 50% | 16.000 | 0.052 | 0.000 | 0.179 | 7.805 | 130.462 | 0.000 | 9.699 | 0.000 | 0.000 |
| 75% | 16.000 | 0.090 | 0.000 | 0.252 | 18.417 | 724.678 | 0.000 | 9.962 | 0.000 | 0.052 |
| max | 16.000 | 0.127 | 0.031 | 0.380 | 131.355 | 27,199.111 | 0.000 | 9.999 | 0.000 | 0.168 |
SUMMARY
DataSet DType Table Chunk_Size
769500 /Grids/G2/precipTotWaterContent/stdev float32 0.000 16.000
| Chunk_Size | Mean | Median | Sd | Skew | Kurt | Min | Max | Q1 | Q3 | |
|---|---|---|---|---|---|---|---|---|---|---|
| count | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 |
| mean | 15.703 | 0.027 | 0.000 | 0.110 | 43.110 | 16,377.813 | 0.000 | 4.423 | 0.000 | 0.011 |
| std | 1.260 | 0.024 | 0.000 | 0.074 | 106.849 | 64,718.794 | 0.000 | 0.605 | 0.000 | 0.022 |
| min | 10.656 | 0.000 | 0.000 | 0.004 | 4.683 | 40.449 | 0.000 | 2.904 | 0.000 | 0.000 |
| 25% | 16.000 | 0.005 | 0.000 | 0.046 | 6.613 | 70.571 | 0.000 | 4.289 | 0.000 | 0.000 |
| 50% | 16.000 | 0.020 | 0.000 | 0.102 | 9.280 | 145.461 | 0.000 | 4.713 | 0.000 | 0.000 |
| 75% | 16.000 | 0.042 | 0.000 | 0.145 | 23.470 | 1,071.809 | 0.000 | 4.826 | 0.000 | 0.003 |
| max | 16.000 | 0.076 | 0.000 | 0.235 | 463.993 | 275,478.034 | 0.000 | 4.910 | 0.000 | 0.079 |
SUMMARY
DataSet DType Table Chunk_Size
798660 /Grids/G2/surfPrecipTotRateDiurnal/count int32 0.000 16.000
| Chunk_Size | Mean | Median | Sd | Skew | Kurt | Min | Max | Q1 | Q3 | |
|---|---|---|---|---|---|---|---|---|---|---|
| count | 9.000 | 9.000 | 9.000 | 9.000 | 9.000 | 9.000 | 9.000 | 9.000 | 9.000 | 9.000 |
| mean | 15.703 | 0.382 | 0.000 | 2.219 | 7.923 | 72.593 | 0.000 | 43.778 | 0.000 | 0.000 |
| std | 0.891 | 0.073 | 0.000 | 0.254 | 0.697 | 13.507 | 0.000 | 7.513 | 0.000 | 0.000 |
| min | 13.328 | 0.259 | 0.000 | 1.749 | 7.097 | 57.656 | 0.000 | 34.000 | 0.000 | 0.000 |
| 25% | 16.000 | 0.350 | 0.000 | 2.145 | 7.288 | 60.960 | 0.000 | 40.000 | 0.000 | 0.000 |
| 50% | 16.000 | 0.376 | 0.000 | 2.235 | 8.107 | 75.221 | 0.000 | 42.000 | 0.000 | 0.000 |
| 75% | 16.000 | 0.450 | 0.000 | 2.426 | 8.293 | 78.616 | 0.000 | 49.000 | 0.000 | 0.000 |
| max | 16.000 | 0.464 | 0.000 | 2.490 | 9.268 | 99.990 | 0.000 | 57.000 | 0.000 | 0.000 |
SUMMARY
DataSet DType Table Chunk_Size
813240 /Grids/G2/surfPrecipTotRateDiurnal/mean float32 0.000 16.000
| Chunk_Size | Mean | Median | Sd | Skew | Kurt | Min | Max | Q1 | Q3 | |
|---|---|---|---|---|---|---|---|---|---|---|
| count | 9.000 | 9.000 | 9.000 | 9.000 | 9.000 | 9.000 | 9.000 | 9.000 | 9.000 | 9.000 |
| mean | 15.703 | 0.075 | 0.000 | 0.585 | 45.298 | 9,108.544 | 0.000 | 174.894 | 0.000 | 0.000 |
| std | 0.891 | 0.018 | 0.000 | 0.110 | 26.393 | 12,017.392 | 0.000 | 27.034 | 0.000 | 0.000 |
| min | 13.328 | 0.049 | 0.000 | 0.389 | 21.534 | 1,038.742 | 0.000 | 126.668 | 0.000 | 0.000 |
| 25% | 16.000 | 0.066 | 0.000 | 0.548 | 33.069 | 3,453.626 | 0.000 | 164.262 | 0.000 | 0.000 |
| 50% | 16.000 | 0.073 | 0.000 | 0.579 | 39.312 | 4,815.003 | 0.000 | 179.099 | 0.000 | 0.000 |
| 75% | 16.000 | 0.082 | 0.000 | 0.613 | 41.770 | 6,277.041 | 0.000 | 185.010 | 0.000 | 0.000 |
| max | 16.000 | 0.104 | 0.000 | 0.756 | 107.043 | 39,446.954 | 0.000 | 210.769 | 0.000 | 0.000 |
SUMMARY
DataSet DType Table Chunk_Size
827820 /Grids/G2/surfPrecipTotRateDiurnal/stdev float32 0.000 16.000
| Chunk_Size | Mean | Median | Sd | Skew | Kurt | Min | Max | Q1 | Q3 | |
|---|---|---|---|---|---|---|---|---|---|---|
| count | 9.000 | 9.000 | 9.000 | 9.000 | 9.000 | 9.000 | 9.000 | 9.000 | 9.000 | 9.000 |
| mean | 15.703 | 0.049 | 0.000 | 0.510 | 26.690 | 1,513.963 | 0.000 | 76.010 | 0.000 | 0.000 |
| std | 0.891 | 0.018 | 0.000 | 0.148 | 9.859 | 1,580.024 | 0.000 | 24.781 | 0.000 | 0.000 |
| min | 13.328 | 0.021 | 0.000 | 0.262 | 18.284 | 479.720 | 0.000 | 52.397 | 0.000 | 0.000 |
| 25% | 16.000 | 0.040 | 0.000 | 0.482 | 22.515 | 777.610 | 0.000 | 61.558 | 0.000 | 0.000 |
| 50% | 16.000 | 0.047 | 0.000 | 0.516 | 23.762 | 962.616 | 0.000 | 66.855 | 0.000 | 0.000 |
| 75% | 16.000 | 0.051 | 0.000 | 0.523 | 28.078 | 1,278.061 | 0.000 | 87.402 | 0.000 | 0.000 |
| max | 16.000 | 0.077 | 0.000 | 0.737 | 51.318 | 5,616.062 | 0.000 | 133.155 | 0.000 | 0.000 |
SUMMARY
DataSet DType Table Chunk_Size
842400 /Tair_2m float32 0.000 16.000
| Chunk_Size | Mean | Median | Sd | Skew | Kurt | Min | Max | Q1 | Q3 | |
|---|---|---|---|---|---|---|---|---|---|---|
| count | 91.000 | 91.000 | 91.000 | 91.000 | 91.000 | 91.000 | 91.000 | 91.000 | 91.000 | 91.000 |
| mean | 15.907 | -492.861 | -637.851 | 507.856 | 0.008 | -1.997 | -999.000 | 30.934 | -999.000 | 19.359 |
| std | 0.885 | 11.484 | 481.442 | 0.261 | 0.046 | 0.002 | 0.000 | 0.748 | 0.000 | 0.467 |
| min | 7.559 | -509.377 | -999.000 | 506.906 | -0.112 | -1.999 | -999.000 | 29.715 | -999.000 | 18.537 |
| 25% | 16.000 | -500.751 | -999.000 | 507.769 | -0.025 | -1.999 | -999.000 | 30.217 | -999.000 | 18.981 |
| 50% | 16.000 | -497.201 | -999.000 | 507.882 | 0.025 | -1.998 | -999.000 | 30.961 | -999.000 | 19.338 |
| 75% | 16.000 | -484.812 | -3.709 | 507.998 | 0.039 | -1.996 | -999.000 | 31.538 | -999.000 | 19.614 |
| max | 16.000 | -462.618 | -0.219 | 508.295 | 0.073 | -1.987 | -999.000 | 32.856 | -999.000 | 20.471 |
SUMMARY
DataSet DType Table Chunk_Size
989820 /msft/table.index int64 1.000 5.087
| Chunk_Size | Mean | Median | Sd | Skew | Kurt | Min | Max | Q1 | Q3 | |
|---|---|---|---|---|---|---|---|---|---|---|
| 989820 | 5.087 | 333,376.500 | 333,376.500 | 192,475.301 | -0.000 | -1.200 | 0.000 | 666,753.000 | 166,688.250 | 500,064.750 |
SUMMARY
DataSet DType Table Chunk_Size
991440 /msft/table.values_block_0 float64 1.000 16.000
1001160 /msft/table.values_block_0 float64 2.000 16.000
| Chunk_Size | Mean | Median | Sd | Skew | Kurt | Min | Max | Q1 | Q3 | |
|---|---|---|---|---|---|---|---|---|---|---|
| count | 6.000 | 6.000 | 6.000 | 6.000 | 6.000 | 6.000 | 6.000 | 6.000 | 6.000 | 6.000 |
| mean | 13.565 | 2,081,623.879 | 33.159 | 8,049,441.324 | 4.057 | 15.290 | 0.833 | 39,586,285.667 | 33.055 | 117.667 |
| std | 5.964 | 1,410,956.193 | 0.139 | 4,886,982.358 | 0.580 | 5.429 | 0.408 | 19,712,436.404 | 0.113 | 72.345 |
| min | 1.391 | 351,120.326 | 33.016 | 1,567,594.696 | 3.320 | 9.041 | 0.000 | 11,157,436.000 | 32.950 | 63.000 |
| 25% | 16.000 | 1,226,555.500 | 33.047 | 4,935,090.450 | 3.706 | 11.862 | 1.000 | 27,689,908.500 | 32.975 | 86.250 |
| 50% | 16.000 | 1,868,692.201 | 33.130 | 7,848,154.048 | 3.990 | 14.560 | 1.000 | 40,646,306.500 | 33.009 | 93.500 |
| 75% | 16.000 | 2,851,883.458 | 33.280 | 11,204,784.394 | 4.371 | 17.424 | 1.000 | 53,356,485.500 | 33.146 | 106.000 |
| max | 16.000 | 4,209,114.739 | 33.330 | 14,745,014.725 | 4.931 | 24.234 | 1.000 | 64,103,344.000 | 33.210 | 262.000 |
SUMMARY
DataSet DType Table Chunk_Size
1010880 /msft/table.values_block_1 int64 1.000 5.087
| Chunk_Size | Mean | Median | Sd | Skew | Kurt | Min | Max | Q1 | Q3 | |
|---|---|---|---|---|---|---|---|---|---|---|
| 1010880 | 5.087 | -4.000 | -4.000 | 0.000 | 0.000 | -3.000 | -4.000 | -4.000 | -4.000 | -4.000 |
SUMMARY
DataSet DType Table Chunk_Size
1012500 /msft/table.values_block_2 |S49 1.000 16.000
1025460 /msft/table.values_block_2 |S49 2.000 16.000
| Chunk_Size | Mean | Median | Sd | Skew | Kurt | Min | Max | Q1 | Q3 | |
|---|---|---|---|---|---|---|---|---|---|---|
| count | 24.000 | 24.000 | 24.000 | 24.000 | 24.000 | 24.000 | 24.000 | 24.000 | 24.000 | 24.000 |
| mean | 15.579 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 |
| std | 2.064 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 |
| min | 5.889 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 |
| 25% | 16.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 |
| 50% | 16.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 |
| 75% | 16.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 |
| max | 16.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 |
Para evitar que los diagramas de caja esten plagados de datos atÃpicos, procedemos a filtrar con el codec blosclz, filtro shuffle, nivel de compresión 5 y tamaño de bloque automático para buscar con detenimiento datos atÃpicos.
In [8]:
df_outliers = my_df[(my_df.Block_Size == 0) & (my_df.CL == 5) & (my_df.Codec == 'blosclz') & (my_df.Filter == 'noshuffle')]
cst.paint_dtype_boxplots(df_outliers)
Mostramos a continuación los datos atÃpicos
In [9]:
for i in range(2):
dfaux = df_outliers[df_outliers.DType.str.contains(cst.TYPES[i])]
if dfaux.size > 0:
cr_lim = cst.outlier_lim(dfaux['CRate'])
cs_lim = cst.outlier_lim(dfaux['CSpeed'])
ds_lim = cst.outlier_lim(dfaux['DSpeed'])
result = dfaux[(dfaux.CRate < cr_lim[0]) | (dfaux.CRate > cr_lim[1]) |
(dfaux.CSpeed < cs_lim[0]) | (dfaux.CSpeed > cs_lim[1]) |
(dfaux.DSpeed < ds_lim[0]) | (dfaux.DSpeed > ds_lim[1])][cst.ALL_FEATURES]
if result.size > 0:
print('%d %s OUTLIERS' % (result.shape[0], cst.TYPES[i].upper()))
display(result)
71 FLOAT OUTLIERS
| Chunk_Size | Mean | Median | Sd | Skew | Kurt | Min | Max | Q1 | Q3 | CRate | CSpeed | DSpeed | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 84244 | 1.475 | 7.835 | 6.866 | 6.404 | 0.414 | 0.209 | -12.035 | 27.183 | 4.119 | 11.317 | 1.000 | 1.355 | 26.268 |
| 202504 | 2.215 | 0.650 | 0.000 | 2.098 | 4.997 | 32.385 | 0.000 | 43.932 | 0.000 | 0.000 | 3.036 | 2.835 | 28.800 |
| 307804 | 16.000 | 0.000 | 0.000 | 0.000 | 0.000 | -3.000 | 0.000 | 0.000 | 0.000 | 0.000 | 170.639 | 17.152 | 25.590 |
| 309424 | 16.000 | 0.000 | 0.000 | 0.000 | 0.000 | -3.000 | 0.000 | 0.000 | 0.000 | 0.000 | 170.639 | 18.281 | 24.536 |
| 315904 | 16.000 | 0.020 | 0.000 | 0.301 | 26.479 | 1,032.815 | 0.000 | 36.053 | 0.000 | 0.000 | 46.348 | 13.947 | 17.120 |
| 317524 | 16.000 | 0.000 | 0.000 | 0.000 | 0.000 | -3.000 | 0.000 | 0.000 | 0.000 | 0.000 | 170.639 | 16.753 | 23.406 |
| 319144 | 16.000 | 0.000 | 0.000 | 0.000 | 0.000 | -3.000 | 0.000 | 0.000 | 0.000 | 0.000 | 170.639 | 15.915 | 21.168 |
| 325624 | 16.000 | 0.001 | 0.000 | 0.041 | 108.763 | 21,889.348 | 0.000 | 14.807 | 0.000 | 0.000 | 106.105 | 16.545 | 22.007 |
| 327244 | 16.000 | 0.000 | 0.000 | 0.000 | 0.000 | -3.000 | 0.000 | 0.000 | 0.000 | 0.000 | 170.639 | 18.014 | 24.210 |
| 328864 | 16.000 | 0.000 | 0.000 | 0.000 | 0.000 | -3.000 | 0.000 | 0.000 | 0.000 | 0.000 | 170.639 | 15.773 | 23.019 |
| 330484 | 10.656 | 0.000 | 0.000 | 0.000 | 0.000 | -3.000 | 0.000 | 0.000 | 0.000 | 0.000 | 170.625 | 16.440 | 25.751 |
| 336964 | 16.000 | 0.000 | 0.000 | 0.000 | 0.000 | -3.000 | 0.000 | 0.000 | 0.000 | 0.000 | 170.639 | 18.034 | 26.641 |
| 338584 | 16.000 | 0.000 | 0.000 | 0.000 | 0.000 | -3.000 | 0.000 | 0.000 | 0.000 | 0.000 | 170.639 | 18.134 | 23.962 |
| 345064 | 16.000 | 0.012 | 0.000 | 0.220 | 27.880 | 1,090.685 | 0.000 | 30.342 | 0.000 | 0.000 | 56.946 | 14.668 | 19.035 |
| 346684 | 16.000 | 0.000 | 0.000 | 0.000 | 0.000 | -3.000 | 0.000 | 0.000 | 0.000 | 0.000 | 170.639 | 18.119 | 23.223 |
| 348304 | 16.000 | 0.000 | 0.000 | 0.000 | 0.000 | -3.000 | 0.000 | 0.000 | 0.000 | 0.000 | 170.639 | 16.765 | 23.448 |
| 354784 | 16.000 | 0.001 | 0.000 | 0.041 | 135.611 | 45,023.028 | 0.000 | 25.404 | 0.000 | 0.000 | 115.345 | 15.818 | 20.467 |
| 356404 | 16.000 | 0.000 | 0.000 | 0.000 | 0.000 | -3.000 | 0.000 | 0.000 | 0.000 | 0.000 | 170.639 | 17.027 | 25.303 |
| 358024 | 16.000 | 0.000 | 0.000 | 0.000 | 0.000 | -3.000 | 0.000 | 0.000 | 0.000 | 0.000 | 170.639 | 18.129 | 24.130 |
| 359644 | 10.656 | 0.000 | 0.000 | 0.000 | 0.000 | -3.000 | 0.000 | 0.000 | 0.000 | 0.000 | 170.625 | 15.650 | 23.812 |
| 395284 | 16.000 | 0.000 | 0.000 | 0.000 | 0.000 | -3.000 | 0.000 | 0.000 | 0.000 | 0.000 | 170.639 | 18.618 | 24.291 |
| 396904 | 16.000 | 0.000 | 0.000 | 0.000 | 0.000 | -3.000 | 0.000 | 0.000 | 0.000 | 0.000 | 170.639 | 19.213 | 25.062 |
| 403384 | 16.000 | 0.003 | 0.000 | 0.047 | 23.164 | 799.779 | 0.000 | 5.665 | 0.000 | 0.000 | 46.359 | 13.835 | 17.514 |
| 405004 | 16.000 | 0.000 | 0.000 | 0.000 | 0.000 | -3.000 | 0.000 | 0.000 | 0.000 | 0.000 | 170.639 | 18.461 | 24.976 |
| 406624 | 16.000 | 0.000 | 0.000 | 0.000 | 0.000 | -3.000 | 0.000 | 0.000 | 0.000 | 0.000 | 170.639 | 17.698 | 23.092 |
| 413104 | 16.000 | 0.000 | 0.000 | 0.009 | 94.256 | 16,687.977 | 0.000 | 3.280 | 0.000 | 0.000 | 106.024 | 13.391 | 20.858 |
| 414724 | 16.000 | 0.000 | 0.000 | 0.000 | 0.000 | -3.000 | 0.000 | 0.000 | 0.000 | 0.000 | 170.639 | 18.741 | 29.323 |
| 416344 | 16.000 | 0.000 | 0.000 | 0.000 | 0.000 | -3.000 | 0.000 | 0.000 | 0.000 | 0.000 | 170.639 | 18.235 | 23.936 |
| 417964 | 10.656 | 0.000 | 0.000 | 0.000 | 0.000 | -3.000 | 0.000 | 0.000 | 0.000 | 0.000 | 170.625 | 14.176 | 21.025 |
| 424444 | 16.000 | 0.000 | 0.000 | 0.000 | 0.000 | -3.000 | 0.000 | 0.000 | 0.000 | 0.000 | 170.639 | 15.604 | 21.729 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 505444 | 10.656 | 0.001 | 0.000 | 0.020 | 31.921 | 1,046.805 | 0.000 | 1.158 | 0.000 | 0.000 | 124.932 | 16.366 | 19.958 |
| 513544 | 16.000 | 0.000 | 0.000 | 0.005 | 36.416 | 1,874.125 | 0.000 | 0.755 | 0.000 | 0.000 | 91.924 | 16.483 | 16.965 |
| 523264 | 16.000 | 0.000 | 0.000 | 0.006 | 35.745 | 2,060.796 | 0.000 | 1.340 | 0.000 | 0.000 | 93.883 | 16.396 | 17.708 |
| 532984 | 16.000 | 0.000 | 0.000 | 0.006 | 32.862 | 1,758.761 | 0.000 | 1.340 | 0.000 | 0.000 | 88.083 | 16.516 | 17.430 |
| 534604 | 10.656 | 0.000 | 0.000 | 0.001 | 245.611 | 72,752.224 | 0.000 | 0.239 | 0.000 | 0.000 | 163.987 | 16.434 | 25.230 |
| 571864 | 16.000 | 0.046 | 0.000 | 0.602 | 13.027 | 168.189 | 0.000 | 9.994 | 0.000 | 0.000 | 63.591 | 13.708 | 15.992 |
| 581584 | 16.000 | 0.037 | 0.000 | 0.548 | 14.787 | 217.128 | 0.000 | 9.968 | 0.000 | 0.000 | 71.489 | 14.969 | 16.672 |
| 591304 | 16.000 | 0.104 | 0.000 | 0.898 | 8.499 | 70.373 | 0.000 | 9.968 | 0.000 | 0.000 | 32.714 | 10.660 | 14.410 |
| 592924 | 10.656 | 0.024 | 0.000 | 0.430 | 18.154 | 327.643 | 0.000 | 9.178 | 0.000 | 0.000 | 84.390 | 14.458 | 18.879 |
| 601024 | 16.000 | 0.001 | 0.000 | 0.018 | 29.018 | 991.612 | 0.000 | 1.494 | 0.000 | 0.000 | 90.779 | 15.942 | 18.974 |
| 610744 | 16.000 | 0.001 | 0.000 | 0.017 | 31.752 | 1,158.950 | 0.000 | 1.320 | 0.000 | 0.000 | 93.474 | 15.142 | 16.821 |
| 620464 | 16.000 | 0.001 | 0.000 | 0.019 | 26.873 | 853.465 | 0.000 | 1.350 | 0.000 | 0.000 | 68.705 | 15.608 | 16.693 |
| 622084 | 10.656 | 0.000 | 0.000 | 0.002 | 134.109 | 30,370.566 | 0.000 | 0.865 | 0.000 | 0.000 | 143.297 | 16.148 | 21.407 |
| 659344 | 16.000 | 0.007 | 0.000 | 0.137 | 75.209 | 15,562.234 | 0.000 | 50.185 | 0.000 | 0.000 | 66.125 | 12.632 | 16.740 |
| 669064 | 16.000 | 0.012 | 0.000 | 0.318 | 165.356 | 85,613.265 | 0.000 | 236.366 | 0.000 | 0.000 | 73.083 | 15.796 | 17.107 |
| 678784 | 16.000 | 0.013 | 0.000 | 0.254 | 63.531 | 9,977.393 | 0.000 | 78.317 | 0.000 | 0.000 | 58.243 | 14.990 | 16.307 |
| 680404 | 10.656 | 0.001 | 0.000 | 0.080 | 199.003 | 66,273.386 | 0.000 | 41.717 | 0.000 | 0.000 | 124.774 | 16.106 | 20.647 |
| 688504 | 16.000 | 0.002 | 0.000 | 0.069 | 352.831 | 235,551.444 | 0.000 | 59.261 | 0.000 | 0.000 | 91.939 | 12.891 | 17.170 |
| 698224 | 16.000 | 0.005 | 0.000 | 0.206 | 113.694 | 29,804.922 | 0.000 | 108.006 | 0.000 | 0.000 | 93.751 | 16.659 | 18.409 |
| 707944 | 16.000 | 0.005 | 0.000 | 0.186 | 123.599 | 39,095.443 | 0.000 | 108.006 | 0.000 | 0.000 | 88.041 | 14.874 | 16.723 |
| 709564 | 10.656 | 0.000 | 0.000 | 0.011 | 497.825 | 316,831.759 | 0.000 | 9.596 | 0.000 | 0.000 | 164.040 | 15.326 | 22.757 |
| 746824 | 16.000 | 0.003 | 0.000 | 0.044 | 33.190 | 2,436.183 | 0.000 | 9.328 | 0.000 | 0.000 | 66.161 | 14.629 | 16.882 |
| 756544 | 16.000 | 0.004 | 0.000 | 0.081 | 31.888 | 1,500.975 | 0.000 | 9.504 | 0.000 | 0.000 | 73.059 | 16.126 | 16.282 |
| 766264 | 16.000 | 0.004 | 0.000 | 0.075 | 29.641 | 1,394.054 | 0.000 | 9.445 | 0.000 | 0.000 | 58.167 | 12.659 | 16.262 |
| 767884 | 10.656 | 0.001 | 0.000 | 0.024 | 131.355 | 27,199.111 | 0.000 | 8.050 | 0.000 | 0.000 | 124.753 | 15.832 | 19.529 |
| 775984 | 16.000 | 0.001 | 0.000 | 0.016 | 80.657 | 11,490.593 | 0.000 | 4.715 | 0.000 | 0.000 | 91.987 | 14.956 | 18.703 |
| 785704 | 16.000 | 0.002 | 0.000 | 0.045 | 42.993 | 2,257.347 | 0.000 | 4.450 | 0.000 | 0.000 | 93.832 | 16.917 | 18.306 |
| 795424 | 16.000 | 0.001 | 0.000 | 0.042 | 44.040 | 2,384.775 | 0.000 | 4.133 | 0.000 | 0.000 | 88.062 | 16.791 | 18.089 |
| 797044 | 10.656 | 0.000 | 0.000 | 0.004 | 463.993 | 275,478.034 | 0.000 | 2.904 | 0.000 | 0.000 | 163.955 | 15.177 | 22.639 |
| 1006024 | 16.000 | 43.093 | 32.990 | 2,488.719 | 250.340 | 63,998.655 | 0.000 | 688,643.000 | 32.990 | 32.990 | 38.938 | 9.159 | 6.579 |
71 rows × 13 columns
37 INT OUTLIERS
| Chunk_Size | Mean | Median | Sd | Skew | Kurt | Min | Max | Q1 | Q3 | CRate | CSpeed | DSpeed | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 150664 | 0.738 | 46,750.635 | 42,412.000 | 42,964.463 | 1.123 | 2.123 | 0.000 | 211,383.000 | 121.000 | 79,434.750 | 1.000 | 0.901 | 26.761 |
| 199264 | 2.445 | 0.000 | 0.000 | 0.027 | 99.485 | 18,684.148 | 0.000 | 8.000 | 0.000 | 0.000 | 156.816 | 9.576 | 22.110 |
| 212224 | 2.445 | 0.002 | 0.000 | 0.055 | 50.616 | 3,993.813 | 0.000 | 8.000 | 0.000 | 0.000 | 134.316 | 9.476 | 16.129 |
| 231664 | 16.000 | 0.000 | 0.000 | 0.000 | 0.000 | -3.000 | 0.000 | 0.000 | 0.000 | 0.000 | 170.639 | 18.661 | 29.037 |
| 251104 | 2.445 | 0.001 | 0.000 | 0.045 | 47.237 | 3,683.632 | 0.000 | 7.000 | 0.000 | 0.000 | 135.187 | 9.414 | 23.567 |
| 278644 | 16.000 | 0.000 | 0.000 | 0.000 | 0.000 | -3.000 | 0.000 | 0.000 | 0.000 | 0.000 | 170.639 | 18.661 | 25.934 |
| 280264 | 16.000 | 0.000 | 0.000 | 0.000 | 0.000 | -3.000 | 0.000 | 0.000 | 0.000 | 0.000 | 170.639 | 17.896 | 24.426 |
| 288364 | 16.000 | 0.000 | 0.000 | 0.000 | 0.000 | -3.000 | 0.000 | 0.000 | 0.000 | 0.000 | 170.639 | 18.276 | 26.037 |
| 289984 | 16.000 | 0.000 | 0.000 | 0.000 | 0.000 | -3.000 | 0.000 | 0.000 | 0.000 | 0.000 | 170.639 | 16.596 | 26.309 |
| 296464 | 16.000 | 0.017 | 0.000 | 0.468 | 41.152 | 2,111.449 | 0.000 | 63.000 | 0.000 | 0.000 | 120.436 | 13.216 | 20.252 |
| 298084 | 16.000 | 0.000 | 0.000 | 0.000 | 0.000 | -3.000 | 0.000 | 0.000 | 0.000 | 0.000 | 170.639 | 17.351 | 24.121 |
| 299704 | 16.000 | 0.000 | 0.000 | 0.000 | 0.000 | -3.000 | 0.000 | 0.000 | 0.000 | 0.000 | 170.639 | 18.709 | 26.511 |
| 301324 | 10.656 | 0.000 | 0.000 | 0.000 | 0.000 | -3.000 | 0.000 | 0.000 | 0.000 | 0.000 | 170.625 | 13.999 | 24.927 |
| 366124 | 16.000 | 0.000 | 0.000 | 0.000 | 0.000 | -3.000 | 0.000 | 0.000 | 0.000 | 0.000 | 170.639 | 15.984 | 23.060 |
| 367744 | 16.000 | 0.000 | 0.000 | 0.000 | 0.000 | -3.000 | 0.000 | 0.000 | 0.000 | 0.000 | 170.639 | 18.270 | 25.148 |
| 375844 | 16.000 | 0.000 | 0.000 | 0.000 | 0.000 | -3.000 | 0.000 | 0.000 | 0.000 | 0.000 | 170.639 | 16.655 | 19.441 |
| 377464 | 16.000 | 0.000 | 0.000 | 0.000 | 0.000 | -3.000 | 0.000 | 0.000 | 0.000 | 0.000 | 170.639 | 17.351 | 22.963 |
| 383944 | 16.000 | 0.017 | 0.000 | 0.468 | 41.147 | 2,110.792 | 0.000 | 63.000 | 0.000 | 0.000 | 120.441 | 16.860 | 20.812 |
| 385564 | 16.000 | 0.000 | 0.000 | 0.000 | 0.000 | -3.000 | 0.000 | 0.000 | 0.000 | 0.000 | 170.639 | 16.483 | 18.886 |
| 387184 | 16.000 | 0.000 | 0.000 | 0.000 | 0.000 | -3.000 | 0.000 | 0.000 | 0.000 | 0.000 | 170.639 | 16.795 | 20.196 |
| 388804 | 10.656 | 0.000 | 0.000 | 0.000 | 0.000 | -3.000 | 0.000 | 0.000 | 0.000 | 0.000 | 170.625 | 16.527 | 28.528 |
| 455224 | 16.000 | 0.019 | 0.000 | 0.411 | 38.029 | 1,880.823 | 0.000 | 45.000 | 0.000 | 0.000 | 84.332 | 15.237 | 15.742 |
| 464944 | 16.000 | 0.014 | 0.000 | 0.314 | 37.427 | 1,912.485 | 0.000 | 43.000 | 0.000 | 0.000 | 90.207 | 14.375 | 15.028 |
| 474664 | 16.000 | 0.019 | 0.000 | 0.386 | 36.527 | 1,832.705 | 0.000 | 58.000 | 0.000 | 0.000 | 76.793 | 12.598 | 13.806 |
| 476284 | 10.656 | 0.001 | 0.000 | 0.042 | 57.967 | 6,928.863 | 0.000 | 11.000 | 0.000 | 0.000 | 138.803 | 15.093 | 18.029 |
| 542704 | 16.000 | 0.020 | 0.000 | 0.417 | 37.539 | 1,836.680 | 0.000 | 44.000 | 0.000 | 0.000 | 81.900 | 13.736 | 15.826 |
| 552424 | 16.000 | 0.014 | 0.000 | 0.316 | 37.139 | 1,884.219 | 0.000 | 43.000 | 0.000 | 0.000 | 88.748 | 15.945 | 15.438 |
| 563764 | 10.656 | 0.004 | 0.000 | 0.078 | 30.801 | 1,542.716 | 0.000 | 11.000 | 0.000 | 0.000 | 107.874 | 13.239 | 15.826 |
| 630184 | 16.000 | 0.019 | 0.000 | 0.412 | 38.020 | 1,880.003 | 0.000 | 45.000 | 0.000 | 0.000 | 84.316 | 15.377 | 15.398 |
| 639904 | 16.000 | 0.014 | 0.000 | 0.314 | 37.406 | 1,910.042 | 0.000 | 43.000 | 0.000 | 0.000 | 90.192 | 15.294 | 16.433 |
| 649624 | 16.000 | 0.019 | 0.000 | 0.386 | 36.518 | 1,831.651 | 0.000 | 58.000 | 0.000 | 0.000 | 76.787 | 13.131 | 15.220 |
| 651244 | 10.656 | 0.001 | 0.000 | 0.042 | 57.967 | 6,928.863 | 0.000 | 11.000 | 0.000 | 0.000 | 138.803 | 16.577 | 20.245 |
| 717664 | 16.000 | 0.019 | 0.000 | 0.411 | 38.027 | 1,880.604 | 0.000 | 45.000 | 0.000 | 0.000 | 84.322 | 14.036 | 14.800 |
| 727384 | 16.000 | 0.014 | 0.000 | 0.309 | 37.074 | 1,887.380 | 0.000 | 43.000 | 0.000 | 0.000 | 90.257 | 16.290 | 16.198 |
| 737104 | 16.000 | 0.019 | 0.000 | 0.382 | 36.250 | 1,814.581 | 0.000 | 58.000 | 0.000 | 0.000 | 76.801 | 13.535 | 16.094 |
| 738724 | 10.656 | 0.001 | 0.000 | 0.042 | 57.993 | 6,934.303 | 0.000 | 11.000 | 0.000 | 0.000 | 138.828 | 16.653 | 21.449 |
| 1010884 | 5.087 | -4.000 | -4.000 | 0.000 | 0.000 | -3.000 | -4.000 | -4.000 | -4.000 | -4.000 | 118.928 | 9.746 | 4.296 |
No mostramos los datos atÃpicos de tipo string dado que no extraemos ninguna caracterÃstica de chunk que podamos comentar, nos centraremos en ellos cuando busquemos correlaciones entre blosclz y el resto de codecs.
En cuanto a los datos atÃpicos observamos que la mayorÃa son series números idénticos o muy parecidos, siempre con un rango intercuartÃlico de 0.
Correlaciones Block Size¶
Aquà pretendemos observar la correlación entre el tamaño de bloque y las medidas de compresión, para ello filtramos los datos por tipo, codec, filtro, nivel de compresión y tamaño de bloque; y calculamos la media de su ratio de compresión y velocidades de compresión/decompresión.
In [10]:
cst.paint_all_block_cor(my_df, 'shuffle', c_level=5)
In [11]:
cst.paint_all_block_cor(my_df, 'noshuffle')
In [12]:
cst.paint_cl_comparison(my_df, 'shuffle', 'blosclz')
In [13]:
cst.paint_cl_comparison(my_df, 'shuffle', 'lz4')
Comparación de niveles de compresión¶
Al igual que en el anterior caso hacemos los mismos gráficos pero observando el nivel de compresión.
In [14]:
# BLOCK SIZE --> CL
cst.paint_all_block_cor(my_df, 'shuffle', block_size=256, cl_mode=True)
In [15]:
cst.paint_all_block_cor(my_df, 'noshuffle', block_size=256, cl_mode=True)
Tablas columnares VS Tablas normales¶
En el caso de que los datos esten en forma de tabla, si la tabla contiene más de una columna se realizan dos pruebas de compresión, una guardando los datos como tabla normal, fila por fila y otra guardándolos columnarmente.
In [16]:
df_col = my_df[my_df.Table == 2]
if df_col.size > 0:
sets = df_col.drop_duplicates(subset=['DataSet'])
for dataset in sets['DataSet']:
dfaux = my_df[my_df.DataSet == dataset]
normal_table = dfaux[dfaux.Table == 1][cst.TEST_FEATURES]
normal_table.columns = ['N_CRate', 'N_CSpeed', 'N_DSpeed']
col_table = dfaux[dfaux.Table == 2][cst.TEST_FEATURES]
col_table.columns = ['COL_CRate', 'COL_CSpeed', 'COL_DSpeed']
result = pd.concat([normal_table, col_table])
result = result[['N_CRate', 'COL_CRate', 'N_CSpeed', 'COL_CSpeed','N_DSpeed', 'COL_DSpeed']]
print(sets[sets.DataSet == dataset][cst.DESC_SET])
display(result.describe())
DataSet DType Table Chunk_Size 1001160 /msft/table.values_block_0 float64 2.000 16.000
| N_CRate | COL_CRate | N_CSpeed | COL_CSpeed | N_DSpeed | COL_DSpeed | |
|---|---|---|---|---|---|---|
| count | 9,720.000 | 9,720.000 | 9,720.000 | 9,720.000 | 9,720.000 | 9,720.000 |
| mean | 10.462 | 17.942 | 1.840 | 2.016 | 5.685 | 4.757 |
| std | 7.310 | 33.079 | 1.959 | 2.131 | 4.262 | 3.132 |
| min | 1.000 | 1.000 | 0.002 | 0.002 | 0.369 | 0.361 |
| 25% | 4.342 | 5.133 | 0.398 | 0.392 | 2.550 | 2.527 |
| 50% | 8.844 | 8.050 | 1.196 | 1.433 | 4.122 | 4.010 |
| 75% | 14.532 | 15.924 | 2.503 | 2.987 | 7.891 | 6.330 |
| max | 39.004 | 297.005 | 9.296 | 14.213 | 29.984 | 29.984 |
DataSet DType Table Chunk_Size 1025460 /msft/table.values_block_2 |S49 2.000 16.000
| N_CRate | COL_CRate | N_CSpeed | COL_CSpeed | N_DSpeed | COL_DSpeed | |
|---|---|---|---|---|---|---|
| count | 12,960.000 | 12,960.000 | 12,960.000 | 12,960.000 | 12,960.000 | 12,960.000 |
| mean | 52.373 | 147.258 | 4.985 | 5.591 | 11.729 | 11.048 |
| std | 45.062 | 737.652 | 4.778 | 5.360 | 4.194 | 3.841 |
| min | 1.000 | 1.000 | 0.005 | 0.007 | 1.749 | 1.953 |
| 25% | 7.624 | 21.154 | 1.081 | 1.008 | 9.844 | 9.404 |
| 50% | 46.981 | 47.146 | 3.405 | 3.806 | 13.373 | 12.361 |
| 75% | 72.699 | 86.410 | 8.698 | 9.543 | 14.908 | 14.073 |
| max | 234.129 | 10,131.164 | 17.067 | 19.993 | 26.164 | 22.071 |
Correlaciones Blosclz-CL1 VS Otros¶
Para poder visualizar todas estas correlaciones calculamos directamente el coeficiente de pearson y su p-valor asociado entre los datos de blosclz con nivel de compresión 1 y el resto.
In [17]:
cst.paint_codec_pearson_corr(my_df, 'blosclz', 1)
In [18]:
cst.paint_codec_pearson_corr(my_df, 'lz4', 1)
In [19]:
dfaux = my_df[(my_df.Codec == 'lz4') & (my_df.Block_Size == 256) & (my_df.Filter == 'shuffle') &
(my_df.CL == 5) & (my_df.DType.str.contains('float') | my_df.DType.str.contains('int'))]
cols = ['Mean', 'Sd', 'Skew', 'Kurt']
cst.custom_pairs(dfaux, cols)
625 points
In [20]:
# TODO N_STREAKS
cols = ['Range', 'Q_Range']
dfaux = dfaux.assign(Range=dfaux['Max'] - dfaux['Min'])
dfaux = dfaux.assign(Q_Range=dfaux['Q3'] - dfaux['Q1'])
cst.custom_pairs(dfaux, cols)
625 points
In [ ]: